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AMER. ZOOL., 30:629-705 (1990)
Rethinking Mammalian Brain Evolution1
TERRENCE W. DEACON
Harvard University, Peabody Museum, Cambridge, Massachusetts 02138
SYNOPSIS. A critical review of past and current theories of mammalian brain evolution
is presented in order to discuss conceptual problems that persist in the field. Problems
with the concept of homology arise because of the interaction of cell lineages and axonal
connectivity in the determination of structural features of the brain. Focusing on the
continuity of information represented by ontogenetic mechanisms as opposed to morphological features avoids many of these problems and suggests homological relationships
that otherwise have gone unnoticed. Many apparently progressive trends and parallelisms
in mammalian brain evolution turn out to result from the influence of underlying developmental homologies. Confusions about evolutionary advancement, increasing architectonic differentiation, and the evolution of new brain structures result from a failure to
appreciate how increasing brain size can bias developmental processes with respect to
axonal competition, increased cellular metabolic demands and decreased information
processing efficiency. Explanations of the evolution of novel structures and new connectional patterns are criticized for their failure to consider the constraints of neural developmental processes. The correlations between structural neogenesis, functional specialization and size changes in brain evolution are explained by a theory of competitive
displacement of neural connections by others during development under the biasing
influences of differential allometry, cell death or axon-target affinity changes. The "displacement hypothesis" is used to propose speculative accounts for the differential enlargement and multiplication of cortical areas, the origins of mammalian isocortex, the unusual
features of dolphin cortex and the dramatic structural and functional reorganizations that
characterize human brain evolution.
INTRODUCTION
Intrinsic difficulties
Despite the fact that the evolution of the
brain—particularly the human brain—is
of intrinsic interest to anyone curious about
the human mind and the origins of human
nature, the scientific study of brain evolution is not a major subdiscipline within
biology, psychology, anthropology or even
the neurosciences. The apparently poor
representation of this area of study in the
sciences can in part be attributed to the
paucity of direct paleontological evidence
regarding brain evolution and the longtime inaccessibility of crucial comparative
neuroanatomic details. The poor representation of information about brain evolution in disciplines outside the neurosciences is additionally limited by the
considerable sophistication in comparative
neuroanatomy and physiology that is
1
From the Symposium on Science as a Way of Knowing—Neurobiology and Behavior organized by Edward
S. Hodgson and presented at the Centennial Meeting
of the American Society of Zoologists, 27-30 December 1989, at Boston, Massachusetts.
required to even begin to grapple with the
questions in a meaningful way. The disturbing correlate of this is that speculative
theories concerning brain evolution—
especially human brain evolution—are
widespread and often contain relatively little neuroanatomical or neurophysiological
information. But even theories conceived
by neuroanatomists and neurophysiologists often reflect numerous unsupported
assumptions about the direction of evolutionary trends, the nature of natural selection affecting brain processes, the ways that
brains can vary from one species to another,
the relationship between structure and
function within the brain and even the
nature of intelligence itself. Although
paleoneurology is unlikely to experience
sudden advances in the years to come, many
of the barriers to relevant neuroanatomical evidence have dissolved in the wake of
the introduction of many new experimental techniques in recent decades. Now that
many of the technical impediments standing in the way of detailed knowledge about
brain structure and function have been
removed, many of these hitherto unquestioned assumptions are now open to test.
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TERRENCE W. DEACON
Among colleagues in the neurosciences
one sometimes hears the criticism that,
unlike most other areas of neuroscience,
the study of brain evolution is limited to
theory because it is essentially beyond the
reach of experimental approaches.
Although brains of extinct species are not
available for direct inspection and analysis,
this does not necessarily mean that theories
of brain evolution are empirically untestable. Indeed they are every bit as susceptible to experimental investigation and
testing as are other theories of brain organization and function. The approach must
necessarily be indirect, but it need be no
less effectual nor any less scientific or
experimental. We should remember that
the vast majority of scientific data in any
field is indirect, irrespective of whether the
object under study is directly observable.
From tracks left by subatomic particles in
nuclear accelerators to the measurements
of minute amounts of unseen biochemicals
registered in scintillation counters, nearly
all of the "hard data" generated in the
laboratories of any field of the natural sciences are indirect and circumstantial. It is
not the directness or indirectness of the
data that is important, rather it is the
repeatability of the findings and the coherence of many lines of evidence that are
crucial to scientific knowledge.
A good analogy to the study of brain
evolution is provided by the study of gene
evolution. Modern techniques for analyzing and comparing base sequences of DNA
molecules from living organisms are beginning to provide a truly astronomical fund
of information concerning both molecular
and organismal evolution. Without analyzing a single fossil specimen of DNA we are
nonetheless capable of reconstructing large
fractions of the genomes of extinct species,
characterizing major gene duplication and
reorganization events of the distant past,
and predicting the ancestral lineages of living species and the approximate dates of
their divergences. Al this is available today
despite the fact that only miniscule portions of the DNA in even the best studied
species are actually known and virtually
nothing is directly known about the DNA
of most species. This level of analysis is
made possible by the immensity and complexity of the existing genomes. In many
cases even direct fossil evidence of apparent phylogenetic relationships has been
abandoned in the face of contrary molecular information. As nearly limitless sources
of correlative molecular evidence are fed
into phylogenetic analyses in the near
future they will become immensely more
reliable for the determination of phylogeny than the best of all possible fossil finds.
Living organisms are incredibly complex
systems at all levels of scale. Each molecular and organ system within an organism
embodies within its design the ubiquitous
mark of its particular evolutionary history.
In addition, the processes of embryogenesis that direct the construction of these
systems are themselves products and symptoms of an evolutionary past that at various
levels intersects with the ancestries of other
species. Comparisons of the differences and
similarities among molecular systems,
organ systems and developmental processes in different species provide an almost
limitless source of information for investigating the evolution of biological structures. This is ultimately the final arbiter of
any analysis of evolutionary relationships—even for paleontological data—
since the interpretation of fossils is only as
accurate and complete as the information
we have about living counterparts.
The complexity of the vertebrate brain
rivals or exceeds the complexity of all the
other organ systems of the body considered together. Because of this we should
expect that information derived from the
brains of living species will be more than
adequate to the task of investigating brain
evolution, so long as we are willing and able
to approach the task with the level of
sophistication demanded by it. Given this
complexity and our still primitive understanding of brain organization and function, we must be prepared to integrate
information from a variety of subfields of
neuroscience and evolutionary biology in
order to begin to approach the problems
of brain evolution with any clarity.
Although numerous researchers since the
RETHINKING MAMMALIAN BRAIN EVOLUTION
nineteenth century have pursued the study
of brain evolution, most have focused on
a single source of evidence to support their
theories, including: relative brain size (e.g.,
apparent trends in brain size increase); features of cortical surface morphology (e.g.,
the appearance or reorganization of sulci);
relative sizes of macroscopic brain structures with respect to one another (e.g., the
apparent enlargement of isocortex with
respect to limbic cortex in presumed
"advanced" brains); or cyto- and myeloarchitectonic features (e.g., the apparent
enlargement of association cortex in the
cerebral cortices of "advanced" species).
But uni-dimensional approaches are almost
certain to lead into one misleading cul-desac after another. This has been the fate
of many past theories, just as it will surely
also be the fate of the corresponding unidimensional theories of the present. The
only hopeful approach is to integrate relevant information from many lines of neurobiological research that bear on the
questions of the patterns of variation and
constraint exhibited by the brains of different species.
Experimental approaches
A number of recent technical advances
have significantly augmented the information previously available to comparative
neuroanatomists. Unlike many other organ
systems, the functionally relevant features
of brain anatomy are entirely microscopic
and for many decades were nearly impossible to distinguish even under the microscope. The axonal connections linking
neuron to neuron, though visible for short
distances in Golgi-stained material (available since the turn of the century), have
only become amenable to study in recent
decades. In the 1950s techniques were perfected for visualizing degenerating axons.
With these techniques it was possible to
identify the general patterns of long axonal
connections in the brains of experimental
animals. However, the resolution and sensitivity of these techniques were insufficient to resolve many of the finer details
of axonal connection patterns. Beginning
in the mid 1970s a number of axonal trac-
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ing techniques were developed that took
advantage of the in vivo uptake and axonal
transport of amino acids, macromolecules
and certain fluorescent dyes. These techniques have now made it possible to investigate the organization of axonal circuitry
in full microscopic detail. In this regard
the most basic functional anatomy of the
brain has at last become available for study.
We are still far from possessing a complete
connectional characterization for even the
best studied of mammalian brains, yet
already the scattered details from comparative studies have begun to provide a
remarkable array of new insights into the
patterns of brain diversity.
Now that tracer techniques have filled
this crucial gap in information about basic
neural functional anatomy, these data can
be integrated with data from physiological
and quantitative studies to provide all the
pieces of evidence necessary for investigating the principles underlying brain evolution. However, it is insufficient to apply
the analysis to adult brains only. Probably
the most crucial information for evolutionary purposes is how connection patterns and structural differentiation are initially established in a developing brain. New
techniques for labeling mitotic cells, marking cell lineages, and experimentally altering development in neonatal animals or in
utero by removing or transplanting embryonic tissues are also beginning to provide
detailed information about the developmental processes that shape neural circuits.
Developmental information can play a crucial role in settling questions of homology.
More generally, it can provide evidence for
the range of possible mechanisms available
for natural selection to modify and demonstrates the constraints that limit possible
variation. Many scenarios of brain evolution conceived in the absence of critical
information about the development of the
structures in question turn out to be incompatible with these constraints.
This rapidly growing body of neurobiological information is providing an
unprecedented opportunity to discover new
patterns of similarity and variation in brain
evolution, and to test old and new hypoth-
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TERRENCE W. DEACON
eses about neural evolutionary processes.
It also provides impetus for a critical reexamination of the dogmas and unanalyzed
assumptions that currently dominate
thinking about brain evolution.
influence they exert over contemporary
ideas about brain function in general. The
first of these is the concept of homology—
the relationship shared by structures by
virtue of sharing a common ancestry. The
second is the notion of evolutionary progress
Conceptual problems
or orthogenesis—the idea that evolution
Most of the theories concerning brain proceeds in a particular direction of
evolution in the early part of the 20th cen- improvement or development. The third
tury focused on its most studiable features, is the significance of brain size—both in
size, gross morphology, and cytoarchitec- absolute terms and relative to the body or
ture. Crude connectional information was to component brain structures. The fourth
available only from careful dissections and is the problem of identifying and explainwhat little could be discerned with Golgi- ing neogenesis—the evolution of new strucstaining. Despite the unavailability of cru- tures and functions.
cial categories of information, theories of
All four conceptual issues were familiar
brain evolution have flourished. As a result to the 19th century pioneers in this field
modern students entering this field will find whose major assumptions in all these areas
the literature replete with numerous well remain dominant in many contemporary
accepted dogmas about the general char- treatments of the subject. Contemporary
acter of brain evolution espoused by some versions of these ideas are in the backof the century's most brilliant comparative ground of every theory of brain function
neuroanatomists. Even more daunting is as well as every attempt to articulate a thethe fact that many of these dogmas have ory of brain evolution. Despite a century
become seamlessly woven into the anatom- of advances in evolutionary thinking in
ical and functional terminology of the rest other fields of biology these ideas within
of the neurosciences as well. Terms like the neurosciences still carry the distinct
paleocortex, neocortex, primary areas, imprint of late 19th century evolutionism.
secondary areas, projection cortex and And despite a century of experimental
association cortex all bear the stamp of an investigation of brain function these ideas
evolutionary vision that appears beyond still reflect one or the other side of the 19th
question, a part of the unspoken common century debates over associationistic and
knowledge of the neuroscienres. But with holistic theories of mental function. Exorthe recent advent of new tools and a flood cising these influences is one of the central
of new information concerning neural con- purposes of this presentation.
nections, functions and ontogenetic proThe other major purpose is to present
cesses, there has been a growing dissoan
alternative approach to the study of
nance between the new data and some of
brain
evolution that avoids many of these
the well-established principles of brain evoa
priori
assumptions. Many of these
lution. My purpose here is to play devil's
assumptions
have been derived from
advocate; to question even the most well
attempts
to
arrange
the adult nervous sysfounded of these dogmas and adopt the
tems
of
contemporary
species in some sort
heretical stance that many—if not most—
of
evolutionary
sequence
or cladistic denof the traditional assumptions concerning
drogram
so
that
any
two
may
be linked via
neural evolutionary processes are without
some
intermediate
adult
forms.
While this
foundation. Hopefully, the introduction of
is
a
powerful
heuristic
it
tends
to
imply the
a healthy dose of skepticism will allow us
misleading
conclusion
that
the
mechato look at the problem of brain evolution
nisms
for
evolutionary
change
can
be
accuwith fresh eyes.
rately described in terms of the modificaFour major conceptual problem areas will tion of adult forms. This, of course, misses
be reexamined most carefully because of a crucial intervening level of analysis. Ultitheir potential for misdirecting the study mately, the mechanisms of evolutionary
of brain evolution and also because of the change must be explained in terms of the
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RETHINKING MAMMALIAN BRAIN EVOLUTION
ontogenetic processes and developmental
constraints that build brains. Explanations
of evolutionary change that are not cast in
developmental terms are merely disguised
comparative morphological descriptions.
Consequently, the reviews and criticisms
presented will constantly appeal to developmental data to test the plausibility and
consistency of some of the dominant theories of brain evolution. Finally, in the last
two sections many of these developmental
insights will be utilized to outline an alternative ontogenetically based interpretation of the processes underlying brain evolution in mammals. This interpretation of
brain reorganization events—called the
"displacement hypothesis"—suggests that
there is an interdependent relationship
between differential growth of particular
neural cell groups and competitive-regressive processes in brain development that
constrain patterns of brain evolution, often
resulting in parallel or converging trends.
Two particularly enigmatic cases are examined in the last section—dolphin and
human brain evolution—and are interpreted in terms of displacement processes.
Features of these brains that previously
have been difficult to explain or seemed
beyond study become understandable in
terms of the displacement hypothesis.
CLAD.STX COVPAR SON
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PARALLEL HOMOPLASY
CONVERGENT HOMOPLASY
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FIG. 1. Homology relationships. The basic homology relationships as outlined by Northcutt (1984). The
arrows represent descent relationships. The vertical
axis represents comparison over time or descent in
evolution and the horizontal dimension represents
comparison of contemporaneous species. The geometric shapes represent similar or different, ancestral
or derived traits. In the case of parallel homoplasy it
is unclear whether the parallel divergence from the
ancestral condition is a consequence of internal (homological) or external (selectional) commonalities.
These same relationships can be applied equally to
comparisons between lineages or to homologous repetition of parts within an organism. (Redrawn from
Northcutt, 1984.)
applied to traits that exhibit structural or
functional similarities but which are not
derived from common ancestry. In other
words, their similarity is the result of influHOMOLOGY
ences extrinsic to the organism. The other,
The concepts of homology and homoplasy
parallel homoplasy, has traditionally been
The concept of homology in some form termed parallelism and refers to cases where
is essential to any study of evolutionary there is similarity in both form and commorphology. It defines the warp of evo- mon ancestry but where the formal similutionary continuity with respect to which larity between traits is not shared in the
the weft of diverse adaptations can be common ancestral condition. In other
understood. In a useful summary of the words, the formal similarity of the (patrisproblems of homology in comparative neu- tically) homologous parts is presumed to
roscience Northcutt (1984) distinguishes have arisen independently in the two linpatristic homologies (the actual descent rela- eages after divergence from the common
tionship between an ancestral form and a ancestor. In this case there is both a patrispresent form) from cladistic homologies (the tic homological relationship and a cladistic
comparison of extant forms with respect convergent homoplaseous relationship
to their possible common ancestral rela- involved. The parallel divergence of the
tionships) and then contrasts these with two two descendent traits from the common
forms of dishomology that may be con- ancestral condition is presumably the result
fused with homology. One of these, con- of common extrinsic selection pressures.
vergent homoplasy, corresponds to what has These relationships are schematized in Figtraditionally been termed analogy and is ure 1 (redrawn from Northcutt, 1984).
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TERRENCE W. DEACON
Where the ancestral condition is the
unknown feature to be inferred from cladistic comparisons it can be quite difficult
to distinguish homology from these two
forms of homoplasy. Northcutt, following
Wiley (1981) and others, suggests a number of guidelines for aiding this discrimination, including: (1) sharing deep similarities of form (as opposed to merely
superficial resemblances), (2) sharing common epigenetic precedence (i.e., derivation
from common ontogenetic precursor
structures), and (3) the existence of a continuity of intermediate forms in species
intermediate in relationship between the
two being compared. All there criteria are
versions of the identification of similarity
in some form.
In this discussion I will not review the
various problems encountered in attempting to determine neural homologies in
practice, nor will I discuss methodological
strategies for circumventing these problems and the multiple criteria that must be
satisfied to provide a convincing case. These
have been well reviewed elsewhere (Campbell, 1982; Campbell and Hodos, 1970;
Ghiselin, 1976; Northcutt, 1984). The
point of this discussion is to analyze the
concept itself as it is applied to problems
of brain evolution because I think there is
reason to be suspicious of the assumptions
implicit in its common usage. It is not the
empirical determination of homology that
is at issue, but the concept itself. I will argue
that there is something fundamentally
wrong with the notion of homology as it is
applied to the comparison of morphological features that can become especially
troublesome in the analysis of brain structure.
Problems with the concept of homology
Homological relationships are most
clearly exhibited in topological relationships. A focus on topological continuity for
identifying homology dates back to the earliest pre-evolutionary theories about the
vertebrate "Bauplan" (an insightful historical discussion is provided in Russell,
1916). Because organisms are spatially
organized systems, position within a network of relationships is crucial to conti-
nuity of function. Although the particular
features of the individual components of
the organism may change over evolutionary time the systemic relationships among
parts, including their contiguity relationships, are relatively stable. Even when
structures derived from a common evolutionary precursor have diverged in form
so as to share no superficial resemblance
their relationships to other structures
within the organism, both in the adult form
and at various stages of development, will
exhibit sufficient similarity to indicate their
homology.
The usefulness of topological criteria for
the determination of homological similarities derives from the fact that many morphological traits (although not the underlying genes themselves) are determined by
systemic interactions between morphogenetic fields (or other converging morphogenetic influences) and not by independent
local mechanisms. The information that
determines a morphogenetic field inevitably derives from multiple genetic sources
interacting with one another sequentially
and simultaneously during ontogenesis.
The resultant morphological trait is a bit
like a node within a network that has no
independent existence apart from its relative position. Such a node is defined by
its unique convergence of relationships with
other nodes. If some of these relationships
are lost or new ones are gained, continuity
with the previous state becomes ambiguous
and depends on whether you focus on the
relationships or the nodes. Analogously, a
single morphological feature may become
two if interdependent morphogenetic processes decouple in space or time, but two
features may also fuse to become one if
previously noninteracting morphogenetic
processes become subsequently linked and
interdependent.
This possibility is more likely in the brain
than in other organs by virtue of the fact
that brain traits are defined in terms of two
independent topological criteria: (1) cell
lineage relationships of local populations
that may determine local topological relationships, cell structure, and molecular and
neurotransmitter characteristics; and (2)
connectional relationships determined by
RETHINKING MAMMALIAN BRAIN EVOLUTION
axons that link numerous separated targets, each likely derived from different cell
lineages, which may also influence morphological, cellular and functional characteristics of their various target structures. Both cellular and connectional
attributes interact during development to
determine the architecture and function of
a brain region.
Assuming that connectivity is capable of
changing during the course of evolution it
is not hard to imagine the kinds of difficulties that might arise in interpreting
homologies. The position, cellular characteristics and even embryonic origins of
a brain structure in a descendent species
may be derived from a corresponding
structure in some ancestor, and the descendent structure's connectional relationships
may also be derived from connection patterns in that same ancestor. Yet the particular homologous circuit and homologous structure may not have been
associated with each other in the brain of
that ancestor. For example, it is conceivable that a series of evolutionary events can
cause afferents of one brain structure to
invade some other structure, replacing the
"ancestral" afferents of the new target—
similar effects can be induced experimentally (see below). In this event a connectional or circuit homology will have been
maintained, probably retaining its functional characteristics, but the relationship
between cellularly defined homologues and
connectionally defined homologues will
have become dissociated. The structural
homology can no longer be defined with
respect to position within a network and
the connectional homology can no longer
be defined with respect to the structures
that are connected. Continuity with the
ancestral form can nonetheless still be
traced through the remaining descent relationships in each case, though the number
of topological criteria used to identify this
descent has diminished for each.
Further deterioration of homology criteria can also be imagined. For example, if
the connectional relationships play a significant role in determining the local
cytoarchitecture and neurotransmitter
characteristics of a target area (this appears
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to happen in cerebral cortex, as indicated
by heterotopic transplantation experiments) it might appear on these grounds
that an ancestral target has simply become
displaced to a new position, despite the fact
that cell lineages and some connectional
relationships did not follow this shift. With
a large number of criteria in agreement,
but cell lineage and a few connectional
relationships do not follow this shift. With
difficult to decide between the deafferented target or the invaded target as the
appropriate homologue of the ancestral
structure. At the level of the whole structure the judgment is ambiguous and yet
each underlying trait has a homologous pair
that can be traced in unbroken series to a
common ancestral condition. It is not clear
that shifting the analysis to these underlying traits can escape similar problems at
a yet lower level. As we consider evolutionary "interventions" that might alter
progressively earlier stages of ontogeny it
is possible to imagine increasing loss of
descent criteria in this manner.
A similar complication can arise in the
effort to identify homologous sulci in relatively convoluted brains. Prior to the
development of techniques for unambiguously staining myelin or neuron cell bodies the interpretation of sulcal homologies
in different species brains was considered
the best clue to the structural homology of
underlying regions, and this approach
dominated throughout the early part of
this century (Ariens Kappers et al., 1936).
Although it has recently been abandoned
as unreliable for most comparative work,
it still remains the only evidence for paleoneurology (working with the casts of fossilized crania). In the study of human evolution this has been the source for
continuing heated debates over the origins
of "modern" human brain traits {e.g., Falk,
1980, 1983, 1989; Holloway, 1981, 1984,
1985, 1988).
Most cortical sulci are probably the result
of the interactions between the mechanical
forces and constraints imposed by the cranial cavity, differences in growth rates of
brain areas, and relative elasticity of different areas of the developing cortex and
underlying white matter. If the underlying
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TERRENCE W. DEACON
neural substrate influencing the formation
of a sulcus changes or becomes displaced
with respect to cranial landmarks in subsequent lineages it may cause the position
of the sulcus to follow. If this were the
typical case sulci might be relatively good
indicators of underlying brain structure
homologies. Alternatively, changes in bone
growth patterns of the skull or changes of
the absolute size of the brain with respect
to the skull in subsequent lineages may produce changes in mechanical forces influencing sulcal position and cause a sulcus to
shift to a new location without any corresponding change in position of the original
neural substrate. In this case the link
between sulcal and neural homologies
would be broken. However, if the appearance of a particular sulcus is dependent on
the combined influence of both extrinsic
mechanical forces and intrinsic growth
processes of the neural tissue, then spatial
separation of these two independent influences may cause a sulcus to disappear and
then reappear in some later lineage in
which these influences again become
realigned. This atavism would still be a case
of homology, despite the discontinuity in
descent. Finally, if a particular sulcus can
be induced by either influence alone then
spatial separation of extrinsic and intrinsic
factors may also produce two sulci where
one existed previously. In this case,
although each is patristically homologous
with the ancestral condition, it is unclear
whether they can be said to be cladistically
homologous to each other. All of these possibilities demonstrate the dangers of treating sulci as definitive markers of underlying neural homologies.
Similar problems with the strict descent
interpretation of homology have been
noted with respect to non-neurological
comparative problems, causing some
authors (e.g., von Cranach, 1976; Filler,
1986) to suggest that the homology concept should be entirely abandoned. But an
alternative approach is suggested by these
problematic examples. The crucial questions we are trying to answer by identifying
homologies are questions about continuity
of information (Van Valen, 1982). A morphological structure or any other manifest
trait is only the surface expression of
underlying information. This information
is encoded both in gene sequence and in
the topological and temporal conditions of
their expression in the developing organism. The confluence of multiple independent sources and kinds of epigenetic information to form a particular structure
implies that no particular individual thread
of information constitutes an indispensable
link between homologous structures. A
homology exists so long as some relationships between the remaining sources of
information are maintained. Alternatively,
if separate threads of information are
passed down from generation to generation independently and only brought into
relation with one another in some descendent where their interaction produces a
novel structure, we must consider the
structure as emergent and neoplastic (the
newly established relationships between
these threads of information is itself a bit
of information that is unprecedented) and
yet also recognize the complete homology
of underlying component morphogenetic
processes. The relationship is diagrammed
in Figure 2. Because ontogenetic processes
are multileveled, homological relationships must also be multileveled (Alberch,
1982; Fasolo and Malacarne, 1988), with
homologies at higher levels not necessarily
reduceable to those at lower levels. In addition, homologies at every level above that
of the genes are to some extent ephemeral,
capable of dissolving and reconstituting in
the course of evolution because they are
determined only in relational terms. This
also implies that the same bit of epigenetic
information expressed in a different context within the same organism must also
be understood as homological.
Homologies between the different
parts of a brain
The interpretation of homology as common information is crucial to another classic use of the concept of homology: serial
homology or homological multiplication of parts.
Repeated similar parts in the segments of
a worm, similar vertebrae in different positions along the spinal column, similarities
in limb and digit structure, and bilaterally
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RETHINKING MAMMALIAN BRAIN EVOLUTION
symmetric parts of the body in general are
all examples of homologous repeated parts.
Although not descended from any single
ancestral structure, such homologous parts
undoubtedly inherit their similarities of
form from a single ancestral source of
developmental information. Within the
central nervous system there are examples
of classic serial homology in segmental
spinal cord circuitry, bilaterally symmetric
parts at all levels, and multiple homologous
parts within every structure at many levels
of organization.
Starting on a small scale we recognize
that nearly all neurons exhibit homologous
parts: axons, dendrites, synapses, etc.
Within the same structure there are classes
of neurons with homologous patterns of
dendritic arborization, axonal targets and
neurotransmitters. Local circuit patterns
of nearby neuronal groups also exhibit
homologies, such as are found among cortical lamina and cortical columns in isocortex. Even distributed functional circuits
linking separate structures may be serially
homologous: e.g., projections from different thalamic nuclei to different cortical
areas. Even structures that are superficially
quite distinct may exhibit underlying
homologies at some levels but not others.
This might be the case for the relationship
between the hippocampus and the isocortex, which exhibit many features in common at the cellular level and have homologous patterns of afferents and efferents
yet very different laminar architecture.
Homologies between different brain
regions might possibly develop as a result
of derivation from a common undifferentiated ancestral structure, but descent
homology need not be defined at the structural level only. It may also result from
independent expression of the same underlying epigenetic information. Similarly,
during ontogeny homologies between cell
types may develop by descent from a common embryogenetic cell lineage and
homologies between complex structures
may develop because they were each
derived in a process of differentiation from
some common embryological structure.
However, because all cells share the same
genetic information, it is also possible that
onlogenetic interactions
phenotypes
morphological
level
epigenetic
process level
epigenetic
mechanisms
\
/
\
;
ontogenetic interactions
morphological
homoplasy
phenotypes
epigenetic
homologies
intervening
variables
producing
developmental
plasticity
FIG. 2. Developmental homologies. The multiple
level problem of developmental homologies is depicted
in a highly schematic form by representing interacting morphogenetic processes as arrows and the resultant morphological traits as geometric shapes. The
upper figure shows that the homological relationships
could be analyzed either at the morphological level
by comparing morphological (or even behavioral)
phenotypes or at the epigenetic process level by comparing epigenetic mechanisms. Both are phenotypes
that indirectly represent underlying genotypes but
the higher level analysis condenses information represented at the lower level by distinct processes and
can thereby miss considerable underlying developmental homology. Nonetheless the higher level analysis also takes into account conserved or derived relationships between underlying developmental
mechanisms that may themselves be homologous in
two lineages but which produce non-similar diverging
phenotypes. Of course the actual condition involves
many more than two levels. The lower level "epigenetic mechanisms" are likely themselves the products
of relationships between yet lower level cellular or
molecular processes and the "phenotypic level" may
also be a set of epigenetic mechanisms of a higher
level of complexity. Hierarchic analysis cannot be
avoided.
homologous structures may appear simultaneously in development by independent
expression of the same underlying information activated by some common molecular trigger or internal timing mechanism.
Within a number of areas of the brain it
is likely that cell lineage is not the only and
perhaps not even the major determinant
of cellular, structural or functional homologies. For a few brain areas there is now
evidence that a single precurser cell can
give rise to the multiple cell types within
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TERRENCE W. DEACON
that region (Rakic, 1988) and studies utilizing embryonic chimeras composed of two
immunologically distinguishable genomes
demonstrate that cells from both lineages
are effectively scattered diffusely throughout all areas and representing all cell types
(Goldowitz, 1987). Cell lineage probably
determines many local biochemical characteristics of neurons (Fasolo and Malacarne, 1988) and certain structural architectonic features (e.g., Kuljis and Rakic,
1988) and it may provide gross areal differences distinguishing major morphogenetic fields, but at the present time there
is little positive evidence available on this
point and extensive evidence that extrinsic
influences determine function and neural
connectivity to a large degree (O'Leary,
1989). Timing of final mitosis and intercellular interactions also appear to play significant roles in determining neuronal cellular types and local structural and
functional characteristics.
In general, a major part of structural
differentiation in the developing brain is
based on distributed information that is
embodied systemically in its spatial-temporal organization and dynamically in the
axonal interactions between independently derived neuronal populations. The
details of this process will be discussed in
later sections, but in terms of homology
this fact leads to an important conclusion.
If the information distinguishing one
region from others is not entirely embodied within the cells of that region, but is
expressed only as those cells are contacted
by invading axons and as its own axons
establish efferent terminations, then different serially homologous structures
within the brain (e.g., different cortical
regions) do not ultimately determine their
own distinctions of structure and function.
Their unique specializations with respect
to one another are instead derived from
network relationships with other areas of
the brain (both cortical and subcortical).
Functional homology
One last use of the concept of homology
must be introduced at this stage before
moving on to a discussion of some of the
major theories of brain evolution: the con-
cept of functional homology. It can be
defined as the similarity and continuity that
exist between functions as a result of
homologies between their substrates. The
evolution of new functions by the modification of old structures is a common theme
in evolution. When the vertebrate forelimb evolved the capacity for flight in the
evolution of birds, the skeletal, muscular
and neural structures retained the general
"Bauplan" of the ancestral terrestrial condition and also retained numerous functional constraints. These have all played a
role in shaping flying behavior in bird
species. Additionally, the peripheral motor
neural architecture (Sokoloff et al, 1989)
and even features of the central locomotor
"patterns" (Kaplan and Goslow, 1989)
exhibit strong similarities in birds and terrestrial quadrupeds, despite the other
major functional differences that their
exclusive adaptations demanded.
With the differentiation of new neural
circuits from ancestral circuits and the
elaboration of corresponding new functional adaptations we can expect to trace
functional homologies in the form of
underlying functional similarities and constraints. Even in extreme cases in which
neural structure is co-opted for new adaptations that are radically different than the
ancestral function, the underlying homologies will likely exert a major organizing
influence on the form and range of variability of the new function. This may even
be true of such a novel adaptation as the
syntactic structure of language (e.g., Reynolds, 1976; Lieberman, 1984; Deacon,
1990c), if some of the cortical systems that
came to serve language functions in the
course of human evolution had been antecedently adapted for other behaviors (e.g.,
motor planning). Anatomical evidence for
such a view is presented by Deacon (1988a,
1990c).
Functional homologies should also be
exhibited by serially homologous structures within the same brain. For example
the many homologous structural features
shared by all regions of isocortex suggest
that there should be strong functional
homologies shared by all of its functional
subdivisions despite the radical differences
RETHINKING MAMMALIAN BRAIN EVOLUTION
in modality of their input-output relationships (Diamond, 1979). The same may also
be said of the different regions and subdivisions of the basal ganglia (Alexander
and Strick, 1986). Presumably, the afferents to each homologous area transmit distinct forms of information that are subjected to some common neural calculation
in each homologous area. For this reason,
different scenarios for the phylogenetic
ancestry of brain structures that suggest
different ancestor-descent relationships
bring with them different predictions concerning function.
PROGRESSION
The assumption of evolutionary progress
The idea of progressive evolution is a
product of the uneasy marriage between
Darwinism and the scala naturae theories
of the mid 19th century. It received its
clearest expression in the theories of Spencer, Haeckel, Berg and Teilhard de Chardin among other influential writers.
Although evolutionary biologists in recent
decades have learned to rigorously avoid
making such assumptions when thinking
about a particular assemblage of fossils or
a lineage of species, this habit of thought
is not so well entrenched in the neurosciences, nor in anthropology, psychology or
linguistics where theories and assumptions
about human brain evolution are also likely
to be found. The tendency is so pervasive
that evolution is often considered synonymous with progress, whereas evolutionary change without progress, even when
directional, is often not considered evolution at all, merely "drift."
The ubiquity of the idea of progress in
brain evolution can be traced to what we
believe we already know about our own
place in an intellectual chain of being. It
apparently goes without saying that humans
are the smartest species to have ever lived—
never mind that we are not sure what we
mean by "smartest"—and it is also popular
knowledge that human evolution involved
significant brain enlargement. Our brain
must also be the most complex, if for no
other reason than the fact that our abilities
are the most complex of any species. Since
639
we have appeared only recently after a long
period of brain evolution characterized by
less intelligent species, our brain represents the pinnacle of some long evolutionary trend.
From these few assumptions a great many
predictions must follow, and so from the
outset we feel confident in assuming the
answers to a number of central questions:
bigger brains are smarter brains; more
complex brains are more developed brains;
primates are smarter than other species;
our closest relatives, the great apes, are
smarter than other primates; there is an
evolutionary trend toward increased intelligence; more intelligence is always a superior adaptation to less; brain evolution tends
toward increasing complexity and increased
relative brain size; earlier stages of brain
evolution are characterized by more primitive, relatively less differentiated and relatively smaller brains than later stages; parts
of the brain that are relatively undifferentiated are more primitive and parts that
are more complex are more recent; brain
structures that enlarged most in ourselves
and our close ancestors are the most highly
developed and most recent brain structures; the most recent human functions (i.e.,
language) must be controlled by the most
advanced, complex and recent structures
in the brain; etc. All we need to do is to
find out how the data concerning brain size
and brain structure diversity demonstrate
these points! Presumably, whatever features of brain organization we use to compare brains of different species, Homo sapiens should represent the extreme high end
of the scale (however this is defined in each
case). I call this assumption the "Anthropocentric Maxim."
The tenacious hold of anthropocentrism
on our thinking about brain evolution is
great. What is needed is a biological equivalent of the "Copernican Revolution" to
finally shake it loose. Along with this
implicit anthropocentrism we should also
endeavor to root out the tendency to
assume progressive trends in any aspect of
brain evolution, unless and until all alternative explanations have been exhausted.
There undoubtedly are progressive trends
in brain evolution, but to clearly identify
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TERRENCE W. DEACON
them and to understand their significance
we must demonstrate that they are not
merely superficial correlates of other nonprogressive trends. To be able to do so
requires that we first understand these
other trends.
The a priori assumption of "advancement" in evolutionary sequences is a source
of many misunderstandings. Deacon
(1990a) reviews many of the assumptions
about brain evolution that derive from the
notion of evolutionary progress in brain
size. Even theories that do not specifically
invoke the notion of progression nonetheless tacitly assume it in the process of identifying some structures as "advanced" and
others as "primitive." A primitive to
advanced ranking of living organisms or
their structures must ultimately be based
upon independent knowledge of the evolutionary trend in question; otherwise the
argument is circular. But when faced with
structures that leave no fossil evidence
independent evidence is hard to obtain.
One possibility is to assume that the progressive ranking of soft-tissue structures
should correlate with other preserved indicators of the relative primitiveness or
advancement of the organism as a whole.
Overall similarity of traits from living
species to those in early fossil specimens of
some lineage might suggest that the organization of brain structures is also equally
comparable. It is of course necessary to
determine that the resemblances are not
superficial and the result of convergent
evolution. And even when this can be demonstrated there is never any guarantee that
the brain structures in question have been
as conservative as the rest of the morphology. Even the external morphology of
the brain, as may be revealed by endocasts,
cannot be taken as a reliable indicator of
underlying cellular and connectional
homologies. So a primitive external
appearance of modern brains is an untrustworthy indicator of primitive brain organization.
From simple to complex
It seems unquestionable that simpler
brain structure precedes more complex
brain structure in the course of evolution,
and that more highly differentiated brain
structures are more advanced than more
diffusely organized brain structures.
Although we can probably assume that
there are some recent brains that are more
differentiated than any from fifty million
years ago, we cannot safely invert the logic
and assume that the most undifferentiated
contemporary brains are the least derived.
Confounding variables such as absolute size
and specific sensory-motor specializations
may influence relative differentiation, and
problems in assessing homology as well as
sampling biases inherent in the phyletic
representation of species may introduce
spurious correlates of differentiation that
have nothing to do with primitiveness.
In discussions of mammalian evolution
small bodied living insectivores are typically treated as exemplars of the morphologies of ancestral mammals. These socalled "basal insectivores" are assumed to
be "generalized" in their adaptation and
"conservative" with respect to evolutionary trends, although caveats are usually
suggested regarding the fact that each of
these groups represents some rather specialized adaptations as well. The European
hedgehog (Elliot-Smith, 1910; Ariens Kappers et al., 1936; Filimonoff, 1949; Diamond and Hall, 1969; Valverde and LopezMascaraque, 1981; Sarnat and Netsky,
1981) as well as moles, tenrecs and microchiropteran bats have all been cited as possessing conservative brain structure typical
of an "initial" mammal brain (Sanides,
1969, 1970; Le Gros Clarke, 1971; Glezer
et al., 1988). There are unfortunately a
number of circular assumptions in the concepts of "primitive survivor" and "basal
insectivore" (Martin, 1973) that also afflict
the concept of an "initial brain."
Fossil specimens suggest that it is likely
that the eutherian mammal ancestor which
gave rise to the Paleocene-Eocene radiations was of relatively small body size and
probably bore at least a superficial resemblance to modern shrew-like insectivores.
In this regard there is considerable justification for selecting insectivores as exemplary of the ancestral condition. The presumption that the common ancestor was
somehow "generalized" or even that mod-
RETHINKING MAMMALIAN BRAIN EVOLUTION
ern basal insectivores are "generalized"
species seems a little more puzzling,
although it is widely claimed. In many ways
members of these groups represent some
extremes of specialization. Consider, for
example, the echolocation specialization of
microchiropteran bats, the fossorial or
nocturnal specializations of many shrews,
moles and hedgehogs, the aquatic specializations of some exceptional shrews, and
of course the insectivorous specialization
itself. These facts must certainly relate to
their neurological adaptation. Of course
there is every reason to suspect that the
common ancestor of eutherian mammals
was also specialized in some interesting
ways, but given the radical difference in
faunal context and likely niche specialization there may be no corresponding specialization represented in modern species.
The tree shrew Tupaia has been suggested by some as an appropriate living
model for a Paleocene precursor to primates (Le Gros Clark, 1971;Cartmill, 1972,
1974). In terms of its size and many of its
non-neurological features it too might serve
as a reasonable stem mammal model. But
it is usually disqualified as an "initial brain"
model because it possesses a number of
"advanced" brain features, including moderate encephalization and a differentiated
striate cortex and visual association cortex.
The cortex of Erinaceus, the European
hedgehog, is often treated as a model of
an ancestral mammalian cerebral cortex.
Figure 3 depicts some of the known areal
divisions of the hedgehog cortex along with
an even more "primitive" tenrec brain.
Some notable features of the isocortex of
these species as compared to "advanced"
brains include: relatively small size compared to olfactory and limbic cortex, poorly
distinguishable lamination, low level of
myelination, poor differentiation from area
to area, lack of a clearly distinguishable
agranular motor area, poorly granularized
koniocortical sensory areas, vagueness of
boundaries between architectonic areas,
the apparent adjacency of sensory-motor
projection areas with little interdigitated
association cortex, and a relatively thick
layer I (a limbic cortex characteristic) in all
areas of its isocortex. It seems unquestion-
641
able that these brains are near some
extreme in the spectrum of cortical organization among eutherian mammals, but
this may not be conservatism. In fact, on
the basis of brain traits selected for their
value in determining cladistic relationships, Kirsch et al. (1983) find that hedgehogs do not appear to exhibit a preponderance of conserved traits, but just the
opposite, they appear to possess one of the
most derived mammal brains (Johnson,
1988).
It is clearly not the structure of the
hedgehog body that motivates its choice as
an exemplar. It exhibits highly specialized
spiny hairs for predator protection and has
developed the ability to role into a ball with
only its spines exposed, it has relatively
short, stubby limbs specialized for digging,
it has very rudimentary visual abilities with
clearly reduced eyes that are appropriate
to its nocturnal-fossorial habit, and it has
a well developed specialized snout and presumably highly specialized olfactory abilities for insect predation. Campbell (1988)
remarks that if the hedgehog were otherwise the same but possessed a larger more
differentiated brain it would never have
been considered an exemplar of the "initial brain" pattern. Gould (1977) notes that
in general it is unwise to choose the most
undifferentiated extant member of a group
as a representative of its stem ancestor precisely because small bodied fast breeding
forms are likely to be highly derived
r-selected species. The choice of species
with small undifferentiated brains is not so
much motivated by external similarities
with known fossil types as by a priori
assumptions about what is primitive and
what is advanced.
To carry this paradigm to its logical
extension, the hedgehog is probably not
the most extreme case that could be cited.
Zilles and Rehkamper (1988) point out that
Erinaceus is actually somewhat advanced
with respect to some other basal insectivores and therefore might not be the ideal
exemplar of the "Grundtypus" for mammalian brain organization. They note that
the brains of the tenrec Echinops and the
geogaline Geogale exhibit even less encephalization and exhibit fewer progressive fea-
642
TERRENCE W. DEACON
dorsal view
dorsal view
brain of a tenrec
brain of a hedgehog
Centetes
Erinaceus
FIG. 3. Hedgehog and tenrec brains as seen from above and the side labeled to show approximate positions
of the major sensory and somatomotor fields. Isocortex is indicated in gray in the left hemisphere of each
and limbic and olfactory cortex is white in the same hemisphere. Since most of the cortical representation is
unknown for the tenrec and only partially known for the hedgehog specific boundaries between areas are
not indicated. There is no intent to imply either undifFerentiated cortex or the existence of only single
sensory/motor fields. Note the low ratio of isocortex to limbic-olfactory cortex in these brains, especially the
small tenrec brain.
tures than that of Erinaceus. These authors
conclude that Erinaceus is probably "not a
typical representative of a real basal insectivore" (Zilles and Rehkamper, 1988;
emphasis in the original). Only in a context
where evolution is presumed to progress
from simple to complex, from least encephalized to most encephalized, and from
generalized, inflexible and inefficient in
function to specialized, flexible and highly
efficient in function, can the search for the
absolutely simplest mammalian brain be
equated with the search for the ancestral
brain.
There are two general attributes shared
by essentially all the basal insectivores considered primitive in brain organization that
should cause us to be cautious about generalizing from them. First, each of the candidate exemplar species inhabits a nocturnal-fossorial niche. This is probably no
accident. This adaptation has likely produced secondary reduction or dedifferen-
tiation of the visual system and a correspondingly heavy reliance on the olfactory
system. Evolutionary reduction or degeneration of an essentially unused sense
modality may induce dedifferentiation, but
this does not likely follow an exactly
reversed phyletic trend and may produce
structural features that are quite distinct
from ancestral features. How can we be
sure that the relatively undifFerentiated
state of the cortex of these species is representative of a retained primitive state
rather than a recent specialization?
Second, these exemplar species also represent the very lowest limits of mammalian
brain size. This is a problem because many
measures of structural complexity appear
to be strongly correlated with brain size
(Tower, 1954; Haug, 1987; Deacon, 1990a;
and see the following section). Nearly all
the attributes of "primitiveness" of mammalian brains are also typical attributes of
very small brains, while those of "advance-
RETHINKING MAMMALIAN BRAIN EVOLUTION
ment" are only expressed in relatively large
brains. Progressive trends measured with
respect to these small insectivore species
are significantly confounded with the
effects of differences of scale. Also if there
has been prolonged selection for size
reduction in these species there may also
be simplifications of brain structure of a
secondary character which do not necessarily follow a reverse phylogenetic trend.
Cladistic approaches
The cladistic approach to identifying
evolutionary trends offers some hope of
resolving these ambiguities and avoiding
the trap of implicit progressionism. By
replacing the assumption of evolutionary
development and increase in complexity
with a simpler empirically defined dichotomy between conserved and derived conditions one can arrive at a relatively
unbiased criterion for identifying evolutionary trends. The particular characteristics of the trait are irrelevant, only its
presence or absence in different groups is
important. By pairwise comparison of the
presence or absence of traits between
species in progressively more distant outgroups it is possible to decide which traits
can be operationally defined as derived and
which can be defined as ancestral or conservative. Cladistic analysis has wide acceptance as a means for reconstructing phyletic relationships between lineages, but it
has also been used extensively to trace the
ancestry of specific traits. It has been particularly useful for deciding between alternative accounts of a trait's evolution
because it provides a measure of parsimony. For example, Northcutt (1984) uses
the number of mutational events that must
be postulated in order to explain the distribution of certain vertebrate brain traits
according to different theories to decide
which of these theories provide the most
parsimonious accounts.
The Achilles heel of this approach with
respect to brain evolution is that it will
inevitably tend to favor identifying relatively undifferentiated forms as more
primitive and differentiated forms as more
derived. A structure lacking differentiating features will tend to be glossed as sim-
643
ilar across a wider range of species than
one exhibiting a number of easily discriminated features. As a result, despite its
apparently unbiased definition of polarity,
the cladistic approach may be biased, so as
to pick out more generalized and less differentiated traits as characterizing a common ancestor. Evolutionary regression in
certain lineages is potentially a source of
misleading bias as is the correlation of
absolute brain size with structural complexity. Additionally, this approach is sensitive to the effects of convergent or parallel evolution. It will be argued below that
parallelism is a major feature of mammal
brain evolution.
Nonetheless, the cladistic approach is in
some ways self-correcting in this regard. It
can be useful in discerning some of these
biasing influences by using multiple sources
of information pooled to establish most
parsimonious descent relationships and
then reanalyzing individual trends. For
example, the relative primitiveness of the
"basal insectivore" brain can be tested with
respect to three outgroups of mammals
whose phylogenetic affinities are well
known through other cladistic analyses: the
marsupials and the two living monotremes,
the platypus Ornithorhinchus and the
echidna, or spiny anteater, Tachyglossus.
Many of the characteristics of Erinaceus'
brain, including apparent adjacency of
projection areas, minimal association cortex, high ratio of olfactory-limbic cortex
to isocortex, poor laminar distinction, poor
granularization and poor differentiation of
architectonic areas, are not exhibited in
the brains of larger marsupials and monotremes. Have the apparently more
advanced traits also found in these outgroups evolved independently in the larger
brains of all the mammalian lineages? The
more parsimonious interpretation is that
many of these traits were present in some
form in the common ancestor of all mammalian groups long before the recent
eutherian radiations. That they fail to be
exhibited by some of the brains in the
eutherian lineage (e.g., basal insectivores)
and some brains in the marsupial lineage
(e.g., Didelphis virginiana) is not sufficient
evidence to assume that they are derived.
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TERRENCE W. DEACON
Kaas (1989) applies an implicit cladistic
approach to determine which cortical areas
in all mammals can be traced via descent
from a common ancestor. He notes that in
all the major mammalian lineages (eutherian and metatherian) there are distinct
visual, auditory and somato-motor projection areas within isocortex. He concludes
that the common ancestor for all these lineages likely also possessed these differentiated projection areas and not just an
undifferentiated protoisocortex. Based on
this evidence he rules out a widely cited
theory of cortical evolution proposed by
Sanides (1970) that is based on the assumption that generalized undifferentiated isocortex preceded specialized sensory-motor
projection cortices in the course of cortical
evolution. However, to be more explicit,
what has been demonstrated is that discrete somatic, auditory and visual projection areas are expressed in mammal brains
under all existing conditions and sizes,
whereas some areas, particularly many
association areas, fail to be expressed under
many conditions, specifically in small brains.
The classic view that association cortex
is new in comparison to projection cortex
in part derives from the apparent lack of
association cortex in basal insectivore brains
(but this assumption is criticized below) and
its progressive domination of the cortical
surface in "advanced" mammals. However, some of the larger marsupials and
even the echidna appear to exhibit significant expanses of association cortex in
addition to primary sensory-motor projection areas. Apparently, under similar
developmental conditions—large brain
size—this trait is expressed in every lineage of mammals. The common conditions
required for expression of this trait in all
three lineages also lends confidence to the
claim for homology as opposed to parallel
homoplasy.
The failure of basal insectivore brains to
exhibit distinctly segregated association
areas is not sufficient evidence to deny that
this trait is a shared ancestral trait. Nonetheless the appearance of segregated visual,
auditory and somato-motor areas in all
mammal brains is sufficient evidence to
consider them as shared ancestral traits.
Lack of positive evidence is not sufficient
to deny homology but the availability of
positive evidence is sufficient to establish
it.
This can also be applied to questions concerning the origins of somato-motor areas.
Lende (1969) demonstrated that in Didelphis the somatosensory responsive cortex
and the electrically excitable motor cortex
exhibited complete overlap and that in Erinaceus there was a large region of overlap.
More recently some degree of overlap has
also been demonstrated in rats (Donoghue
et al., 1979). In carnivores and primates
(and probably ungulates) these areas are
adjacent but completely segregated into
distinct parallel somatotopic and musculotopic maps. Lende also argued that there
was even some overlap of visual and auditory cortical areas in the opossum (although
this finding has not been replicated). This
suggested to him that the ancestral state of
cortex was characterized by poor areal differentiation in which all the sensory modalities exhibited nearly complete overlap with
one another. However, at least one larger
Australian marsupial, the brush-tailed
opossum Trichosurus, exhibits considerable
segregation of somatic and motor fields
(Haight and Neylon, 1978, 1979) and the
monotremes appear to exhibit complete
segregation of somatic and motor areas.
All of these facts argue against assuming
that the primitive condition was undifferentiated and completely overlapping and
suggest that at least some degree of segregation of these functional zones characterized the common ancestor of all mammal groups.
But negative evidence can be cited to
support the view that the segregation of
somatic and motor modalities is a convergent trait. This evidence comes from variations in somatotopy of the sensory and
motor maps in the different groups. In most
eutherian mammals studied the two fields
are arranged as mirror images of one
another with respect to their common border, and exhibit this pattern even in species
where there is considerable overlap of the
two areas. However, in edentates and marsupials the two maps appear to be arranged
in parallel as well as overlapped (Dom et
RETHINKING MAMMALIAN BRAIN EVOLUTION
al., 1971; Lende, 1963, 1969; MagalhaesCastro and Saraiva, 1971; Royce et al.,
1975; Saraiva and Magalhaes-Castro,
1975), and in a megachiropteran bat {Pteropus poliocephalus) the somatic map appears
inverted from that typical of most other
eutherian mammals (Calford et al., 1985).
The monotremes appear to exhibit characteristics found both in some eutherian
and in some marsupial brains (Bohringer
and Rowe, 1977). Furthermore, the pattern of thalamocortical connections to these
areas differs in eutherian and marsupial
brains.
This negative evidence is inconclusive
because the differentiation of map orientation could occur independent of the segregation of somatic and motor areas. The
unique status of the fox bat and edentate
somatic maps in comparison to other mammalian groups suggests that this is the case.
Map orientation appears to be a derived
condition in these species. Variation of this
trait occurs against the background of
somatic and motor map segregation as an
apparently older and more conservative
trait. The only placental and marsupial
mammals that do not exhibit segregation
of somatic and motor projection areas also
have relatively small brains. This further
suggests that this is a derived condition
contingent on small size and not the ancestral condition.
Problems with these comparisons of cortical areas stem from the fact that the traits
under consideration are not simple and the
variables that correlate with the differential expression of these complex traits have
not been controlled for in the analysis. The
most important of these variables is brain
size, but other factors are also clearly
involved with regard to more subtle features, such as map topography. Failure to
control for these factors inevitably leads to
their being confounded with descent relationships despite the fact that cladistic analysis itself does not prejudge the primitive
or advanced status of a trait. The differential expression of a large number of traits
with respect to brain size or the differential
expression of traits in brains with respect
to sensory specializations can be a serious
problem for cladistic analyses because it
645
vastly increases the probability of convergence and parallelism. In fact, many of the
"advanced" traits of eutherian mammal
brains could have been inherited from the
common ancestor of eutherian mammals
even if that ancestorfailed to exhibit any of these
traits.
All three mammalian groups have likely
inherited neural developmental constraints and tendencies from a common
ancestor that are expressed differentially
in different contexts. It is possible that the
"initial" eutherian brain possessed the
developmental information for these traits
but failed to express them because certain
other conditions of their expression were
not met. Below it will be argued that the
small size of these brains precludes the
developmental expression of cortical parcellation processes necessary to produce
multiple highly differentiated cortical
areas. Also the regression of the visual system in basal insectivores may further
undermine these processes. Despite the fact
that ancestral traits might not be expressed
in an intervening lineage there need be no
interruption of their descent to subsequent
lineages, and their disappearance or reappearance in certain lineages cannot be
attributed to distinct mutation events and
treated as distinct derived conditions. We
should be especially wary of this possibility
in the choice of traits included for cladistic
analysis.
This again underscores the importance
of thinking of homologies in informational
terms. Even if we were to miraculously learn
the details of morphology of the brain of
the true eutherian ancestor we would perhaps still be a long way from understanding
the initial conditions embodied in that initial
brain that still influence the structure and
the evolution of modern brains. What we
ultimately want to know is what information
was embodied in the initial brain and in
the developmental mechanisms that built
it.
Recapitulationism
During embryogenesis there is a definite
progression from smaller poorly differentiated structures to larger highly differentiated structures. Large species with rel-
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TERRENCE W. DEACON
atively differentiated brains must inevitably
pass through developmental stages in which
their brains are small and poorly differentiated. Consequently, the embryonic
stages of larger more differentiated brains
will inevitably bear some superficial resemblance to the adult stage of small poorly
differentiated brains. Structures that differentiate later in development will thus
appear to be added to an otherwise common substrate. Recapitulation assumes that
adult structures in primitive species are
homologous to embryonic structures in
advanced species. Subsequent modifications of brain structure have been added
in the more "advanced" species by extending the ontogenetic process to include
additional later stages. This process of terminal addition was presumed to link the
evolution of species to development in such
a way that a scale of increasing complexity
in evolution was the inevitable outcome of
a progressive increase of developmental
information. Evolution is explained as an
augmentation of ontogenesis and more
"advanced" species are literally assumed to
be further developed.
Despite the enormous theoretical power
of this synthesis, the weight of comparative
and ontogenetic evidence that has accumulated against this doctrine in the last
century is overwhelming. Ontogenetic differences that distinguish different lineages
may occur at any stage of development and
are clearly not constrained to occur in linear sequence with increasing phylogenetic
divergence. Although larger creatures tend
to exhibit longer ontogenesis than smaller
creatures, larger creatures do not have
additional stages added on and highly
derived members of a lineage do not show
more developmental stages than highly
conservative members; differences in
development appear at many corresponding stages. However, despite the patent
failure of this paradigm, numerous unrecognized recapitulationist assumptions still
persist in the literature about brain evolution, primarily in the form of tacit terminal addition assumptions.
Early recapitulationist theories of brain
development suggested that as children
matured they passed through stages of cognitive and emotional development that
corresponded to distinct "grades" of animal consciousness from reptilian to mammalian, from primitive mammal to primate, from primate to primitive human,
and finally through the ascending stages
from "primitive savagery" to modern civilization (e.g., Spencer, 1870; Baldwin,
1895). Various primitive human societies
and criminals were viewed as arrested at
some prior stage of development. Although
the recapitulational structure of the brain
was assumed by many prominent 19th century and early 20th century neurologists
(e.g., Paul Broca, John Hughlings Jackson,
Ivan Pavlov, John Sherrington, and Sigmund Freud) probably the major catalyst
for the formation of a comparative anatomical version of this theory came as a
result of the synthesis of two sources of
neuroanatomical evidence just subsequent
to the turn of the 20th century.
Paul Flechsig's (1901) analysis of myelinstained tissue from human fetuses at various stages of development demonstrated
a progression of myelination of isocortical
areas that began with primary sensory and
motor areas, continued to belt zones around
these areas and culminated in relatively late
myelinating association areas (see Fig. 4).
This sequence was presumed to correlate
with developmental trends in which basic
sensory-motor abilities mature early in
childhood and the "highest" intellectual
abilities only appear late in development.
It was also presumed to correlate with an
evolutionary sequence from species with
only crude sensory-motor habits and
responses, to species with the capability of
complex and flexible associational learning
abilities. The most developed associated
areas were assumed to be the newest in
evolutionary terms and the last to develop
in ontogeny.
At roughly the same time comparative
anatomists, using preparations that visualized neuron cell bodies, produced maps
of the distinguishable cytoarchitectonic
areas of the cerebral cortex for a number
of mammalian species which appeared to
demonstrate homologues for primary pro-
RETHINKING MAMMALIAN BRAIN EVOLUTION
647
jection areas in all species, but no homolateral view
logues for many human association areas
in monkeys and no homologues for many
monkey association areas in other mammals (e.g., Campbell, 1905; Brodmann,
1909). It clearly appeared as though the
developmental trend recapitulated a
sequence of additions of new cortical areas
leading up to the human brain. New, more
developed association areas were apparently added to the brain of succeeding
species at the end of their maturational
development. The unusually long postnatal brain development of humans could also
be explained on the basis of the extra stages
of brain development that were appended
to human ontogeny.
These assumptions corresponded with
the neuropsychological doctrine of the
time. It was assumed that the moment to
moment processing of sensory input
retraced this same hierarchy, developing
from the crude registration of sensory
information in projection areas, to the construction of a perceptual gestalt in sensory
psychic areas, and finally to the elaboration
of multimodal associations with respect to
different remembered perceptions, actions
and emotional experiences in association
medial view
areas. Primitive animals and young chilFIG.
4.
Flechsig's
myelogenesis figure of the human
dren only progressed through the initial
brain is redrawn from the original leaving out Flechstages of this cognitive hierarchy. Although sig's numerical designation of myeloarchitectonic
the developmental recapitulation assump- fields. The darker areas represent the areas that myetion has been abandoned, a more elaborate linate earliest in development and the white areas
version of this basic hierarchic functional represent the areas that myelinate latest. Insular coris partially exposed. Note the progression from
interpretation remains the dominant con- tex
primary to secondary to association areas of cortex.
temporary theory of sensory processing Note also the early myelination of limbic cortical areas
(e.g., Mishkin and Appenzeller, 1987; and pathways.
Maunsell and Van Essen, 1983), despite
many growing inconsistencies and the
availability of alternative interpretations
(e.g., Brown, 1988; Deacon, 1989a; Dia- will primarily address the issue of the
mond, 1982; Optican and Richmond,
developmental sequence, but will return to
1987).
the issue of the terminal addition of corHowever, in hindsight we can see fun- tical areas in a later section.
damental flaws in both forms of evidence
An alternative explanation for the prifor this correlation of ontogenetic devel- mary-secondary-tertiary hierarchy of myopment with apparent phylogenetic devel- elination of cortical areas can be derived
opment. Ultimately I will argue that both from a correlation between the total level
derived from a failure to control for factors of adult myelination achieved by these
having more to do with brain size than with structures and their apparent developphylogenetic progression. In this section I mental schedule. The early myelinating
648
TERRENCE W. DEACON
areas are also the most heavily myelinated
in the adult and the latest myelinating areas
are the least myelinated in the adult
(Bishop, 1959; Sanides, 1970). This rule
also applies to noncortical structures: the
relatively poorly myelinated reticular formation of the midbrain exhibits one of the
latest and longest cycles of myelination of
any system (Yakovlev and Lecours, 1967)
despite the fact that it is without doubt one
of the most conserved structures in the vertebrate brain. This fact clearly contradicts
the recapitulationist assumption.
There are two possible interpretations
of this correlation between total amount
of myelination and the developmental time
course of myelination. First, since these
assessments of myelination are based on
myelin-stained tissue sections in which more
densely myelinated tissue stains darker,
there may be a level of nearly complete
staining opacity reached at an early developmental stage in areas that exhibit maximal myelination whereas this level of
staining opacity may never be achieved by
very poorly myelinated areas. In this case
the apparent heterochrony could be largely
illusory. Alternatively, the deposit of myelin on axons destined to be heavily myelinated could take place at an absolutely faster
rate (which appears to be indicated by the
data of Yakovlev and Lecours, 1967) and
could begin earlier. It is probably an
important corollary that the most heavily
myelinated fibers are also often the largest
diameter axons that project relatively long
distances or to highly specific targets.
There is also an important correlation
between brain size and primitive-advanced
comparisons. Small insectivore and rodent
brains appear to be generally less myelinated overall and exhibit less myeloarchitectonic differentiation from cortical area
to cortical area than the larger brains of
primates (Sanides, 1970). Therefore if
myelination is to be used as a ruler of progression then the least myelinated areas
should be considered the more primitive
areas and the most myelinated areas the
more advanced cortical areas (Bishop,
1959). This runs exactly counter to the
apparent developmental progression.
This apparent phyletic myeloarchitec-
tonic trend as well as other architectonic
trends led Sanides (1969, 1970, 1972) to
propose that the terminal addition of cortical areas in mammalian evolution might
actually be the reverse of that proposed by
the traditional theorists—progressing from
association areas to secondary areas to primary areas as the most highly developed.
Although not a recapitulationist theory,
because it makes no claims for development, it does nonetheless assume terminal
addition in a phylogenetic sense, and an
implicit progression from primitive to
advanced forms based upon the addition
of more highly differentiated structures.
The neuropsychologist Jason Brown (1977,
1988) has elaborated a self-consciously
recapitulationist theory of cortical function that is based upon Sanides' model of
phylogenetic progression, in which
"microgenetic" processes of sensory analysis proceed in a series of stages from limbic to association to specialized cortical
areas rather than the other way around, as
is suggested in traditional models.
Both interpretations are based on misleading correlations that confound a number of factors. The correlation between the
density of myelination in adults, the initiation and rate of myelination in development, and the overall level of myelination
in brains of different sizes suggests that all
of these apparent comparative trends may
have less to do with any evolutionary
sequence and more to do with certain conservative functional and metabolic relationships between axons of differing sizes
and the glia that form their myelin sheaths.
The developmental differentiation of the
brain occurs in the process of increasing
the size of the brain. The evolution of
increasingly differentiated mammal brains
also correlates with the evolution of
increasing brain size. The similarities
between brain development and brain evolution may largely be the result of these
parallelisms. In both, the confusing role of
brain size and its many correlative structural scaling relationships underlies many
of the misidentifications of evolutionary
progress. The nature of some of these
underlying correlations will be the subject
of the next section.
RETHINKING MAMMALIAN BRAIN EVOLUTION
BRAIN SIZE
The influence of anthropocentrism
The significance of brain size is at once
the most broadly debated issue in the study
of brain evolution and probably also its most
ubiquitous, misunderstood and troubling
feature. More has been written about brain
size than about any other topic concerning
brain evolution. Like the notion of evolutionary progress, interest in brain size
owes much to its apparent importance for
understanding human brain evolution.
With a brain roughly three times larger
than a primate of our size should possess,
it is natural to assume that brain enlargement must hold the key to human uniqueness. But is brain enlargement symptom or
cause in this transformation? Is relative
brain size alone the significant difference
or is it a superficial consequence of more
fundamental changes in brain organization? Size is also the easiest feature of the
brain to study and so lends itself to broad
comparative studies and studies of other
possible correlates to brain size that might
be of interest to researchers who otherwise
have little neuroscientific training. But the
tendency to terminate the analysis of brain
difference with measurements of brain size
is a significant impediment to progress in
the investigation of brain evolution, precisely because issues of brain size are inseparable from issues of function and internal
organization in some very fundamental
ways.
649
the largest mammals on earth. If we think
of the brain as a computer and the body
as the many users that are on line, vying
for processing time and memory storage,
then it becomes obvious that the effective
information processing capacity available
for any particular function is constrained
by the number of competing demands from
other sources. This has suggested to many
that the ratio of brain to body is a more
significant measure of available information processing capacity. Despite the fact
that they have absolutely larger brains, elephants and whales have a much lower ratio
of brain to body than humans. But this
observation is also unsatisfactory. Very
small birds and mammals have a higher
ratio of brain to body size than humans
and an even higher ratio of neuron number to body size.
A satisfactory account of comparative
brain size that ranked humans on top (and
thereby preserved the intuition underlying
the Anthropocentric Maxim) was discovered at the end of the 19th century in the
form of allometric analysis. Since that time
the use of "subtraction criteria" based on
empirical brain and body size trends have
been assumed to define that portion of the
brain mass that is functionally correlated
with information processing demands of
the body. Deviations from these trends have
been assumed to respectively indicate
excess or dearth of mental capacity (but
see criticisms below). The importance of
allometric analysis for the investigation of
patterns of relative growth and the secondary correlates of differences in size is
paramount, not just as a criterion of subtraction, but as a tool for drawing attention
to the ways that biological processes can
be affected by changes of scale. Allometric
analysis has produced some remarkable
insights into the problem of relative growth
and has demonstrated some remarkably
regular patterns of size-related brain variation from species to species and from evolutionary epoch to epoch.
The role of brain size in brain evolution
appears deceptively simple. If the brain is
a computing device of some kind, then an
increase in component processing elements should correlate with an increase in
information processing capacity. This intuition appears to be borne out by the unsystematic observation that species with brains
at the small end of the size range for vertebrates exhibit mostly simple and stereotypic behaviors compared to those with
brains toward the large end of the spectrum. This observation is not totally satThe more detailed pursuit of allometric
isfactory. Some mammal brains exceed the relationships has shown that differences of
human brain in total volume and neuron brain size have consequences at every level
number—for example, elephant and whale of neuroanatomical and neurophysiologibrains. However, these species represent cal organization. Not all features of the
650
TERRENCE W. DEACON
brain scale isometrically with size changes
in brain evolution. This has serious functional consequences that have yet to be
appreciated—much less understood—and
serious methodological consequences for
comparative studies because it immensely
complicates the task of determining
homology. A failure to appreciate the
numerous architectonic and functional
consequences of differences in brain size
lies at the heart of numerous misunderstandings about brain evolution, including
issues of progression and the question of
brain size evolution itself.
Is there a trend toward increasing
encephalization?
One of the earliest discoveries concerning brain evolution in mammals was that
mean brain size and relative brain size have
increased with respect to our reptilian
ancestors and have increased since the
beginning of the great eutherian mammal
radiations. This seems to be a clearly progressive trend and suggests that brain size
itself may be an adaptation under selection.
But is it? If it is, then is there selection for
total brain size or for relative brain size?
And what kind of evidence would be necessary to demonstrate one or the other?
The accepted answers to these questions
have been phrased in terms of the evolution of intelligence, and have changed little
in form since well before the turn of the
century.
Brain enlargement, both in absolute and
relative terms, has typically been referred
to as encephalization. Beginning with the
work of Dubois (1913) the term became
associated with a specific mathematical
index: the measure of the relative deviation of the ratio of brain to body size in a
species from the expected ratio for an average animal of the same body size, based on
trends for a given taxonomic group or for
some baseline comparison group (Bonin,
1937; Dubois, 1913; Gould, 1966; Jerison,
1973; Stephan, 1969). Two major applications of these measures of encephalization are the assessment of taxonomic and
phylogenetic differences of relative brain
size and the assessment of species differences in intelligence. Whether or not there
is any meaningful relationship between rel-
ative brain size and intelligence is open to
serious question. There are distinctly different brain-body trends for different
mammalian taxa that can all provide equally
valid—but not concordant—measures of
encephalization for an individual, the
somatic fraction of brain size that is presumed to be allometrically "subtracted" in
the estimation of encephalization cannot
be a simple linear factor as is often assumed,
and deviations from the empirical trend
cannot automatically be assumed to correlate with mental adaptation as opposed
to metabolic, ontogenetic or somatic adaptations (Deacon, 1990a, b). More importantly, the assumption that intelligence—
much less comparative intelligence—is a
single measurable scalar quantity is highly
dubious from either a neurological or an
evolutionary perspective and has never
been adequately supported by comparative
intelligence testing (MacPhail, 1982; Gardner, 1983; Hodos, 1988; Deacon, 1990a).
This issue has been extensively discussed
and debated elsewhere and so will not be
reviewed here, except to point out both
the obvious progressionist and anthropocentric assumptions that are inextricably
bound up with the entire enterprise. Only
the issues surrounding phylogenetic interpretations will be considered here.
Since the appearance of the first vertebrates, brains and bodies in many lineages
have enlarged by orders of magnitude. The
relative sizes of brains and bodies have also
changed. For an animal of a given body size
the ratio of brain to body size has also
increased in a number of lineages, though
not all. These general trends are capped
by the evolution of mammal brains. The
largest brains ever to have existed are now
possessed by whales and the most extreme
values of encephalization ever to have
existed are now exhibited by dolphins and
primates—particularly humans. Within any
taxonomic group of living vertebrates there
is a negative allometric relationship
between brain size and body size, such that
adult individuals with larger body sizes tend
to exhibit lower ratios of brain to body size
than smaller individuals. The scaling relationship is approximately linear when rendered in logarithmic coordinates but the
particular slope and y-intercept of this line
RETHINKING MAMMALIAN BRAIN EVOLUTION
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FIG. 5. Allometric patterns of comparative vertebrate brain size and mammalian brain growth. Graph A,
on the upper left, depicts convex polygons that enclose all points of brain and body size for four major
vertebrate classes (from Jerison, 1973) along with an approximation of the polygon that would have enclosed
the stem mammals. Note the incredible mammalian brain and body size expansion from this precursor group.
Note also the overlap of teleost fishes and lizards and of birds and mammals and the lack of a scala naturae
trend from fish to mammals. Graph B, on the upper right, shows the trend line for carnivores and for the
domestic dog breeds. Arrows indicate measures of encephalization or somatization for a small dog with respect
to the carnivore trend. This is intended to show that "encephalization" differences do not necessarily imply
selection on brain traits. Graph C, on the bottom left, shows an ontogenetic developmental trajectory for
brain and body growth in two typical mammals of different body size. The prenatal phase overlaps completely
for most species. Graph D, on the bottom right, shows ontogenetic curves for different mammalian species
and their relation to interspecific trends. Note that ontogenetic lines overlap for different size species during
the early ontogenetic phase. The shift of ontogenetic curves that distinguishes primates, cetaceans and elephants from other mammals is shown in gray.
differs depending on the taxonomic group
under consideration. Proposals for
explaining the regularity and slope of these
trends include the possibility that brain size
tracks body surface (Snell, 1891; Dubois,
1913; Jerison, 1973), that brain size is constrained by metabolic capacity which is also
negatively allometric (Martin, 1981; Armstrong, 1983), that because brain is derived
from embryonic ectodermal tissue, the
same growth control mechanisms may control mitosis in brain and body surface structures (Deacon, 19906), or that target body
size is controlled by neuron number via a
mechanism common to all mammals, and
possibly all vertebrates (Deacon, 1990(5).
Ultimately no single explanation can
account for the substantial differences in
scaling relationship exhibited at different
taxonomic levels of analysis. Within a
species the allometry is strongly negative
with slope on the order of 0.1 to 0.2,
whereas within a whole order or whole vertebrate class the allometric slope is often
in the range of 0.6 to 0.8. See Figure 5a
and b.
The brain-body allometries of the different living vertebrate classes appear to
652
TERRENCE W. DEACON
be distributed bimodally. The homeothermic classes—birds and mammals—
tend to scale together and the poikilothermic classes—fish, amphibians and reptiles—tend to scale together, with homeotherms exhibiting a much larger
percentage of brain to body at any given
body size (see Fig. 5a). In the evolution of
birds and mammals there has clearly been
an increase in encephalization over the
ancestral reptilian condition as represented by modern reptiles. But using modern species as exemplars for ancestral relationships it cannot be said that there has
been a steady increase in encephalization
from fish to amphibians to reptiles to birds
and mammals. In fact, some of the most
"primitive" fishes, the sharks and rays,
comprising the Chondryichthes, exhibit
levels of encephalization that exceed all
other fishes, amphibians and reptiles, and
overlap the ranges for birds and mammals
(Bauchot et al., 1979). There is no clear
scala naturae of encephalization. In contrast, relative conservatism of the encephalization relationship is demonstrated by
the extensive overlap of encephalization in
different vertebrate classes.
Discerning the progressive encephalization of mammals relative to their reptilian ancestors involves more subtle distinctions, but the major step across this gap
appears to have already been taken by the
time of the common ancestor of metatherians and eutherians (Ulinksi, 1986). In comparison with other mammalian lineages the
marsupials and the insectivores appear to
occupy the low end of encephalization, but
the monotremes appear on a par with the
mean for eutherian mammals. If the small
brained basal insectivores and marsupials
are characteristic in this regard of most
stem mammalian groups then most other
mammalian lineages have exhibited progressive encephalization. Such a trend is
demonstrated in the fossil record and has
been used as support for the claim that
there has been a progressive trend toward
increased intelligence in all these lineages
(Jerison, 1973).
The relatively lower encephalization of
many small bodied forms including basal
insectivores and rodents, may reflect ges-
tational trade-offs involving large litter sizes
and rapid reproductive rates characteristic
of small r-selected species. These reproductive specializations have been correlated with gestational constraints that affect
brain growth (Martin, 1983; Deacon,
1990ft). Similar factors may also be important for understanding marsupial brain size
development. It is possible that low estimates of "basal" encephalization may be
misleading if they incorporate superimposed developmental trade-offs that secondarily reduce encephalization. We
should expect that progressive removal of
these constraints in lineages radiating into
K-selected, larger-body-size niches might
result in an apparent "rebound" of
encephalization. A similar "rebound" effect
has been suggested by Gould (1975) and
Deacon (1990ft) in response to intense
selection for increasing body size in evolution. Breeding experiments demonstrate
that selection on body size produces body
size increase in successive generations with
little correlated increase in brain size
(Atchley, 1984; Riskae/a/., 1984). Deacon
(1990ft) argues that after an initial rapid
evolution of increased body size effected
by modifications of peripheral hormonal
mechanisms, continued stabilizing selection would tend to produce complementary brain size increase as a more stable
central determiner of target body size.
Thus the rapid radiations into more
K-selected, large-body-size niches that
characterized many mammalian lineages in
the Eocene may have provided a biased
sample of species for comparison to more
modern lineages.
The case for an increase in encephalization in primates, elephants and cetaceans
is strengthened by independent evidence.
From very early in embryogenesis all these
species exhibit approximately double the
ratio of brain to body size found in any
other mammal group at a comparable
developmental stage (Count, 1947; Sacher
and Staffeldt, 1974; Martin, 1983; Deacon,
1990ft). This difference is evident at the
earliest stages in which brains are discernable in the developing embryo and so rules
out explaining this encephalization in terms
of the terminal addition of neural tissue
RETHINKING MAMMALIAN BRAIN EVOLUTION
653
late in development (see Fig. 5c, d). Curi- Body size is a highly flexible and ecologiously, the trajectory of brain-body growth cally significant variable, whereas brain size
for all three groups closely overlaps for the is a relatively inflexible and as yet poorly
entire fetal period, suggesting a common understood variable that may or may not
brain growth mechanism. The brain-body correlate with differences in behavior. The
growth trajectories of the remainder of the search for changes in brain structure that
eutherian mammals also all appear to share correlate with addition or subtraction of
a common fetal trajectory, suggesting that neural tissue in evolution in order to
they all share a different common mech- account for encephalization is for this reaanism for determining brain growth. As son probably misguided; we might just as
with the encephalization difference well look for addition or subtraction of the
between homeotherms and poikilotherms, many parts of the rest of the body.
this embryological encephalization difference distinguishing primates, dolphins and Disentangling allometry and progression
elephants from the remainder of the
When organisms get larger, either dureutherian mammals appears as a distinct ing development or in the course of evodiscontinuity without intermediates.
lution, all features of the organism are not
As a final comment on the encephaliza- scaled up isometrically. Even more troution issue it should be pointed out that the blesome for the comparative anatomist is
term itself reflects an underlying bias that the fact that homological relationships can
is part historical and part theoretical. Early appear to change with evolutionary changes
writers often did not clearly distinguish in size. Different structures may radically
absolute brain size from the relation of change size with respect to one another or
brain size to body size in their discussions alter their relative position, structures may
of brain evolution (Gould, 1981). The ulti- radically change shape due to unequal
mate interest has of course all along been growth rates among their parts or single
the explanation of human brain size. But structures may divide or differentiate to
measures of brain size with respect to body become two or more distinct structures.
size are inherently relational. Increased This is the source of one of the most insidencephalization is also decreased somati- ious problems in evolutionary theory: the
zation, and vice versa (see Fig. 5b). One confusion of size related changes with evoneed not necessarily assume neurological lutionary advancement. The secondary
explanations for differences in this rela- effects of change of size can include the
tionship. Although breeds of dogs differ apparent addition of new structures to old
enormously in degree of encephalization or the appearance of increased complexity
(from small highly encephalized dogs to in existing structures. It may be difficult to
large poorly encephalized dogs) no one tell whether an increase in size has caused
doubts that body size is the selected vari- old structures to differentiate and subdiable and brain size the relatively less flex- vide or whether the addition of new strucible parameter. In fact, breeding experi- tures has caused an increase in size.
ments selecting progeny on the basis of
As D'Arcy Thompson pointed out in his
either extremes of brain size or extremes classic treatise on the effects of growth on
of body size demonstrate that selecting for form (1917), most mechanical forces,
body size produces a poor correlated material properties and structural relationresponse in brain size whereas selecting for ships do not change isometrically with
brain size produces a highly correlated changes in size. Geometric effects are most
response in body size (Roderick et ai, 1976; obvious—e.g., surface area to volume—but
Fuller, 1979; Atchley, 1984; Riska et al., also there are changes in the relative vis1984; Kruska, 1987). Given that a large cosity of fluids, diffusion rates of molefraction of the variance in "encephaliza- cules, structural plasticity or rigidity of
tion" within a species can be a consequence materials, rates of chemical reactions, etc.
of selection on body size, why should this Many of these non-geometric scaling allonot also hold for cross-taxa comparisons? metries result from the fact that ultimately
654
TERRENCE W. DEACON
some components of organisms are of fixed
sizes (e.g., molecules and cells). In order to
maintain isometry of functional properties
across major changes in size it is nearly
always necessary for structures to enlarge
at different rates.
Allometric analysis can help control for
the influence of size, allowing one to compare quantitative traits with the effects of
size subtracted. This "criterion of subtraction" is most often assumed to indicate that
some functional relation has been maintained in the face of the change in size.
This does not mean that such changes are
merely passive effects induced by the mass
of the organism. They are inevitably
"internal" facultative or genetic adaptations to the imbalances or weaknesses
induced by the change in size. Many of
these secondary adaptations may be
encoded in the genome of the organism.
However, a trait that is somehow expressed
facultatively, in a size graded manner,
would have obvious advantages over one
that is specific to a given range of sizes. In
these cases size change should be considered the "primary" adaptation and the
correlated reorganizations of structure can
be considered "secondary."
A genetically encoded, size-correlated
trait that has evolved in response to the
functional demands of size change is a paradigmatic example of a derived condition.
But it also represents a conservative feature to the extent that it is necessary to
preserve some ancestral functional relationship. Functional homology is maintained at the expense of structural homology. The reverse scenario is also possible—
structural homology maintained by virtue
of change in supportive functions to keep
pace with the effects of size. Finally, it is
also possible for size change to be the "secondary" adaptation. A change in size can
be secondary to the production of some
correlated effect that has itself become the
trait of primary adaptive significance. This
latter possibility will be suggested in the
case of human brain size enlargement (see
last section).
In cases where size change is primary it
would be inappropriate to consider the
many secondary adaptations as progressive
trends. The fossil evidence clearly demonstrates that with the demise of the dinosaurs, small mammal species rapidly
adapted to fill niches for large bodied
forms. The mammalian radiations can be
seen as markedly asymmetric with respect
to body size (and correlated brain size). The
lower limit of mammalian body size has
probably not been significantly altered since
the Paleocene but the upper limit has probably been extended about a millionfold
compared to a typical basal insectivore!
Brain size necessarily followed this trend,
although the extension of the upper limit
of brain size, due to its negative allometry
with respect to body size, has probably not
exceeded ten thousand times that of a typical basal insectivore brain. Parallel
enlargement trends characterized numerous lineages of mammals.
I will argue that it is this remarkable parallelism, and not some progressive selection for increasing intelligence, that is
responsible for many pseudoprogressive
trends in mammalian brain evolution.
Larger whole animals were being
selected—not just larger brains—but along
with the correlated brain enlargement in
each lineage a multitude of parallel secondary internal adaptations followed.
Allometry of brain traits at many levels
Mammalian brains range in weight from
around a gram to nearly ten thousand
grams. Even within a single lineage like the
anthropoid primates, in which individual
species share many strong similarities in
brain structure, there is more than a hundredfold difference between the smallest
and largest adult brains. Yet most microstructural features change little in size from
brain to brain. The maximum sizes of neuron and glial cell bodies increase slightly
from the smallest to the largest brains, but
nowhere near the thousandfold scaling of
the brains they comprise (Haug, 1987). The
functional constraints on cell volume no
doubt set an asymptotic upper bound on
cell size that the largest neurons are likely
approaching. The apparent tetraploidy of
the giant Betz cells of the human motor
cortex likely indicates that these cells are
already forced to come up with unusual
RETHINKING MAMMALIAN BRAIN EVOLUTION
ways to circumvent certain functional limitations of their large size.
The constraints on neural size also affect
the scaling of higher-order multi-neuronal
structures. For example, the diameter of
cortical columns as well as the number of
neurons within each column seems to
remain almost constant across brain size
variation (Rocket et ai, 1980). This may
also be the reason that many larger scale
morphological features like cortical thickness increase only slightly from the smallest to the largest brains (Rocket et ai, 1980).
As a consequence the disparity between
microstructure organization and macrostructure organization grows incredibly
with increasing size. Since the macromorphology of the brain, including
distinct homogeneous structures and their
various functional subdivisions, is derived
by ontogenetic processes that function at
the microscopic cellular level, this growing
scale difference is inevitably reflected by
changes of large scale structure. Some of
these morphological changes may reflect
distinct adaptations to these new microstructural demands, but it is likely that the
majority are simply the inadvertent consequences of the same ontogenetic processes operating in a vastly larger brain.
655
projection neyon
ce!;body
local circuit neuron
(e g granule cell)
cytoarchitectonic and myeloarchitectonic allom-
FIG. 6. Local relationships contributing to cytoarchitectonic allometry are depicted in this figure from
Deacon (1990i). The top figure depicts the situation
in a relatively small brain and the lower figure depicts
these same relationships in a slightly larger brain. In
small as compared to large brains projection neurons
possess short, small diameter axons, with relatively
less myelin, smaller cell bodies, lower neuron to glia
ratio, higher neuron densities, lower ratios of local
circuit neurons to projection neurons, smaller size
differences between the smallest and largest neurons,
etc.
etry (Deacon, 1990a). The linear and volumetric increase in scale of the brain with
respect to relatively more conservative limits of cellular structure impose new constraints on neural and glial functions. Consider what must happen to homologous
long projection neurons in brains of
increasing size. In order to maintain similar transmission velocities and transmission integrity longer axons need to have
larger diameters and thicker myelin
sheaths. If the target area has also expanded
in volume (as is typically the case as well)
then the terminal arbor of a typical axon
must also increase. This can be a significant
factor given the exponential differential
between linear dimensions and volumes
since it requires a tremendous increase in
length and number of branchings of an
axon to fill a larger volume with the same
density of synapses. To keep pace with the
metabolic and neurotransmission demands
of such an enlarged axonal volume and surface area the cell body of the neuron must
also be enlarged, but since it depends on
the glial cells surrounding it for its metabolic support the relative number of glia
must also increase. The same constraints
are not experienced by small local circuit
neurons. Since local circuits remain relatively constant in volume (increasing no
more than a few hundred percent across
huge brain size differences) these neurons
should have to change relatively little to
compensate for size (see Fig. 6).
Given these two extremes, it becomes
obvious that the local cyto- and myeloarchitecture of many brain structures will
reflect the influence of size. In general, in
large brains as compared to small brains
This is illustrated by what can be called
656
TERRENCE W. DEACON
there should be a number of regular trends:
In large brains there should be (1) some
much larger cell types, but also a much
greater difference between the smallest and
largest cells, (2) a higher glia to neuron
ratio in most regions, (3) a decreased mean
density of neurons but an increase in the
range and variation of densities in different
areas and subareas, (4) a significant increase
in axonal and dendritic arborization to fill
the slightly increased volume of local circuits, (5) higher levels of myelination in
general but a greater difference from the
most to least myelinated areas and subareas, and (6) since some areas may be specialized for longer projecting cells with
large soma and heavily myelinated axons
and other areas only for short projecting
cells or cells with small diameter, poorly
myelinated axons these differences will be
magnified between brain areas. The net
result will be greater architectonic differentiation within an area and between areas
in large brains as compared to small brains.
Few if any of these changes are likely the
result of the evolution of new ontogenetic
mechanisms, but merely the local dynamical responses of cells trying desperately to
do what they would do in any brain. We
can conclude from this that an increase in
of a network might be the average number
of nodes that must be passed through to
find a link between any two arbitrary nodes.
In all but the smallest or lowest connectivity networks the number of connections
tends to vastly outnumber the nodes. If one
wants to increase the number of nodes in
a network while maintaining the same
average connectivity, then the number of
connections that have to be added with each
new node will grow factorially with each
addition. A factorial increase of this sort
will rapidly lead to astronomical numbers,
particularly in large highly connected networks, but even in networks with relatively
low connectivity very large changes in size
will produce the same result. Except in
minimally connected networks, it will
become increasingly difficult to continue
increasing the number of nodes within a
network and retain the same level of connectivity. This concept in diagrammed in
Figure 7.
Now consider these facts about the central nervous system: One neuron may be
connected to thousands of others, according to some estimates; even in small mammalian brains there are probably billions
of neurons; and there is a nearly tenthousandfold difference in volume between the
architectonic complexity with size is not a reli- smallest and largest mammalian brains.
able measure of progression and advancement, These simple statistics make it clear that
either for comparison of the brains of different network allometry must be one of the major
species or for comparisons of different areas factors responsible for the differences in
organization and function that distinguish
within the same brain.
However, there are also a number of size large and small brains. But the brain is not
related changes for which architectonic a maximally interconnected network. Even
reorganization will be unable to compen- in a brain with a billion cells each consate. Probably the most significant among nected to a thousand others at random the
these is a factor that can be called network average number of nodes separating any
allometry (Deacon, 1990a). Network allom- two will be on the order of twenty, and
etry is essentially a geometric principle brains are not nearly so diffusely orgaanalogous to surface to volume allometry. nized. Probably the majority of connecThe size of a network is a function both of tions between areas of mammalian isocorthe number of nodes in the network and tex and other cortical or subcortical areas
the number of connections between nodes. are reciprocal, and the connections within
Networks with every node directly con- the local circuits of the isocortex are probnected to each other node can be consid- ably highly re-entrant and relatively selfered to exhibit a high level of connectivity contained within columnar modules. Also,
whereas networks with each node directly given the relative comparability of local
connected to only one or two others can circuit structure of isocortex from species
be considered low connectivity networks. to species, it is likely that intracortical conOne measure of the average connectivity nections between columns may be limited
657
RETHINKING MAMMALIAN BRAIN EVOLUTION
to neighboring columns and to specific target columns in other areas. It may then be
a bit more useful to think of connectivity
within the cerebral cortex in terms of
columnar modules as nodes rather than in
terms of individual neurons as nodes. A
separate network allometry might apply at
the columnar level since columns increase
in volume, number of axons and dendrites
but not neurons with increasing brain size.
Nonetheless, given the immense differences in scale that must be considered, even
a relatively poorly interconnected cortical
network will have to contend with connectivity trade-offs in order to compensate for
network allometry.
There is clearly a significant increase in
the proportion of white matter to gray
matter in brains of ascending size, but this
is doubtless nowhere near what would be
required for connectional parity to be
network allometry
maintaining local
connectivity only
network aQometry
maintaining global
connectivity
o
maintained. In order to evolve to significantly
larger sizes brains must decrease connectivity.
This trade-off undoubtedly has its costs.
The two most obvious costs of decreasing
connectivity with increasing size are
reduced integration of distributed functions and significantly increased transmission and processing times. Larger brains are
not necessarily more efficient and more powerful
than smaller brains. In fact, these new functional costs of increasing size will demand
new secondary adaptations in order to
compensate in other ways. If a functional
area of cortex becomes enlarged in the
course of evolution the mean interconnectivity of its columns will decrease. This will
decrease the homogeneity and integrity of
activity patterns that can be maintained
within it and increase the time necessary
for neural "calculations" involving the
whole area to be completed. These costs
can be minimized by breaking the one large
area into two relatively independent subareas capable of processing the same information in parallel, so long as they can be
partially integrated with one another by
specific interconnections. Although this
reorganizational strategy may compensate
in part for loss of local integration and processing efficiency it cannot entirely compensate. And, if size continues to increase,
additional parcellations into multiple areas
FIG. 7. The problem of network allometry is represented by the example of a very simple network
(figure from Deacon, 19906). A series of nodes
(depicted as spheres) is connected reciprocally to each
other (depicted by double arrows) in different size
networks with different extremes of connectivity.
Networks on the left exhibit low connectivity and
those on the right exhibit maximum connectivity. N
= total number of nodes; C = total number of reciprocal connections (note that in the nervous system
reciprocal connections are separate connections); Xn
= the number of other nodes to which any one node
is directly connected; Xc = the mean number of connections intervening between any two arbitrary nodes.
The growth of connections to nodes is a factorial
function of the number of nodes in a fully connected
network and a linear function of the number of nodes
in a minimally connected network. Both low and high
connectivity networks require major functional and
structural trade-offs with size increase.
are necessary and both transmission time
and integration costs will continue to
mount. Of course there may also be advantages, including the increased specificity
and reliability afforded by parallel redundant processing, or alternatively, the possibility of subspecialization of different
subdivisions. The evolution of new structure and function as a result of such pro-
658
TERRENCE W. DEACON
cesses will be discussed further in the next
section.
We can conclude that network allometry
may force a variety of secondary reorganizations of cortical architecture, including
parcellation and multiplication of functional areas, as brains enlarge during evolution. This essentially forces large brains
to alter functional strategies for information processing from those effective in small
brains. A further factor to be considered
is the fact that the receptor systems projecting to these cortical areas are also
enlarging along with body and brain size,
although these probably exhibit a negative
allometry with body size. This must also
play a significant role in determining at
what level of scale there will likely be
breakup and parcellation of cortical areas.
It is far from clear to what extent there is
net gain due to new functionality and
increased information storage or net loss
of efficiency and integration due to increasing loss of connectivity as brain size
increases. We will need a better understanding of this trade-off before we will be
able to think clearly about the question of
comparative intelligence and its relationship to brain-body allometry.
Size and parallelism in mammalian
cortical evolution
These complex allometric considerations complicate the evolutionary interpretation of comparative brain morphology. It is not a simple matter to track
morphological changes and assign them
independent evolutionary causes. If in fact
many "emergent" architectonic changes
associated with brain size evolution are
simply the effects of common underlying
cellular mechanisms compensating for the
effects of size, then it can be misleading to
treat them as "new" features. The only
difference in the information utilized in
the ontogenetic process is difference in size
information—in the form of larger axonal
volumes, greater variance of metabolic
demands for different cell types, etc. If we
focus on the deep informational homologies rather than on the surface structural
homologies it is clear that developmental
mechanisms have not been altered, rather
one of the contextual variables to which
these mechanisms is sensitive has changed
value. These changes might be referred to
as secondary facultative adaptations to distinguish them from secondary adaptations
that actually involve the evolution of new
genetic and developmental information.
There is neither progression nor addition
in this sense, and the parallelisms that result
are not properly thought of as parallel
homoplasy.
It is possible that the ontogenetic mechanisms utilized in parcellation of cortical
areas are sensitive to the demands of network allometry. The fact that axonal competition plays the major role in determining area parcellation and afferent and
efferent relationships within the developing cortex (discussed in the next two sections) suggests that facultative mechanisms
may account for a considerable portion of
the structural and connectional response
to this functional demand. But it is likely
that specific genetic adaptations also
become available to streamline the facultative response (via genetic assimilation) to
these demands during the course of evolution. The addition of new secondary
adaptations (derived conditions) is in order
to maintain the same function (conserved
condition), and can be seen as a sort of
"Red Queen effect" to the extent that the
system is working harder and harder to try
and stay in the same place. The parallelisms that have evolved to maintain functional homology across a large range of
brain sizes are likely the result of a combination of underlying ontogenetic homologies shared by all mammals and specific
genetically encoded biases that modify the
responses of these ontogenetic mechanisms differently in different species.
But it is not necessarily safe even to consider these microallometric changes as
facultative adaptations with respect to size
change. All that is demonstrated is a form
of morphogenetic plasticity that is affected
by size. In a related context, Smith-Gill
(1983) distinguishes two general classes of
developmental plasticity that clarify this
point. The first he calls developmental conversion and the second he calls phenotypic
modulation. In developmental conversion,
RETHINKING MAMMALIAN BRAIN EVOLUTION
environmental cues activate alternative
genetic mechanisms that are expressed in
the organism's development. These different genetic expressions may produce alternative morphs by activating or inhibiting
growth processes affecting the structural
development of certain tissues, by changing cell surface affinities or messengerreceptor site relationships in intercellular
communication, or even by inducing
regressive processes, such as programmed
cell death. In phenotypic modulation environmental cues modulate but do not select
among or alter genetic programs. This
produces variation and adjustment of the
expression of genetic information but not
the selection of different alternative genetic
programs. Smith-Gill notes that phenotypic modulation does not necessarily imply
an adaptive response, ". . . adaptiveness of
phenotypic modulation cannot be assumed
unless specific genetic mechanisms can be
demonstrated." It is unlikely that the ontogenetic responses of neural tissues to the
influence of size are produced by specific
genetic alternatives, since these would have
to differ for each range of size and for each
brain region. Rather, the facultative plasticity of neurons in response to the local
effects of size must be a case of phenotypic
modulation. We cannot necessarily assume
that all aspects of this plasticity are adaptive, even in the broad sense of adaptive
with respect to local metabolic and information processing demands. We can only
assume that significantly maladaptive plastic responses will be strongly selected
against.
In conclusion, the evolution of mammals
is clearly characterized by a trend toward
increasing body size with a correlated
increase in brain size, but it is unclear to
what extent there has been additional independent selection for increased brain size
and brain differentiation in different lineages. Even if there is not independent
selection for brain size in a particular lineage, body size correlated increase in brain
size can be expected to produce a series of
architectonic and functional changes due
to the plasticity of developmental processes
at the neuronal-synaptic level. In general,
with respect to brain structure, we should
659
question the assumption that size increase
is caused by addition of new parts, since
the plastic responses of neural development to size change inevitably produce differentiation and subdivision of existing
structures at all supracellular levels of
organization. Although the majority of
architectonic trends in brain organization
in mammalian lineages give the appearance of increasing differentiation and complexity, we cannot disambiguate this from
the effects of local cellular plasticity and
secondary facultative adaptation which
cannot be considered progressive in any
sense. However, precisely because plastic
phenotypic modulation is not necessarily
adaptational, it cannot be assumed that it
will preserve any semblance of functional
isometry. And even if it is adaptational in
most cases it may fail to be so at the
extremes of size, where otherwise predictable metabolic and information processing
demands may significantly diverge from
ancestral patterns. If such inadvertent
departures from functional isometry contribute useful capabilities or potentialities
they may contribute to directional trends
in evolution. Alternatively, if the adaptability of facultative responses induces
genetic assimilation of nongenetic phenotypic modulation mechanisms into genetically based developmental conversion
mechanisms, the changes in response to
size may produce irreversible evolutionary
changes. In this way secondary adaptations
to size may inadvertently provide the raw
materials for the evolution of new functional systems.
NEOGENESIS: T H E EMERGENCE OF
NEW STRUCTURE
The assumption that new adaptations
require new structures
In many ways the fundamental question
that evolutionary theory purports to answer
is how new species with novel structures
and functions come into being in the course
of time. If anything can be called evolutionary progress it is the creation of totally
new adaptations, not just the augmentation
of existing adaptations. New brain areas
with distinct cellular architecture and con-
660
TERRENCE W. DEACON
nectivity appear in some lineages but not
others, and it is almost certain that over
the course of mammalian brain evolution
the number of discrete brain areas in the
most complex brains has steadily increased.
This has suggested to many that new adaptations and the augmentation of existing
structures are accomplished by the addition of new structures to an already functioning brain. But new structure may also
evolve by co-opting or reorganizing existing structures in some way. In this case the
resulting structure may be radically different than its antecedent, and yet combine
both novel attributes and pre-existing features at different levels of organization.
How is it possible to distinguish between
uniquely derived structural additions and
previous structures that have become radically modified?
The history of vertebrate evolution in
general, including mammalian evolution,
exhibits a trend toward diversification of
adaptations. The invasion of new niches
inevitably requires adaptation of perceptual, behavioral and cognitive processes to
meet the new demands. To some extent
these are acquired at the expense of modifying previous neural systems, trading one
function for the other. But the acquisition
of new abilities could also be achieved by
addition of new functions to old with a corresponding elaboration of the brain. Each
species is the culmination of a phylogenetic
sequence of adaptive changes that leave
their traces in the structure of its body and
brain. There is a natural tendency to envision this as an accretionary process that
progressively adds new structure to old.
This impression is supported by the apparent increase in brain complexity that correlates with the scala naturae hierarchy of
species leading from fish to mammals and
from "primitive" mammals to "advanced"
mammals. The definition of evolutionary
progress is at every step completely dependent upon the identification of neogenesis.
One outstanding difference that is presumed to distinguish primitive from
advanced mammalian brains is an increasing number of architectonically and functionally distinguishable cortical areas. The
increase in numbers of cortical areas has
often been cited as an example of both
neogenesis and of progressive evolution,
and is presumed to correlate with the
increased behavioral and cognitive abilities
of advanced species. The acquisition of new
functional abilities is also a central feature
of human mental evolution. We conceive
of ourselves as possessing all of the cognitive abilities of other species and then
some. Particularly novel in the course of
evolution are human linguistic abilities. In
a behavioral sense this capability is clearly
an addition—functional neogenesis. It is
tempting to assume that the addition of
such an unprecedented function necessarily implies the genesis of novel neurological
structures. With respect to apparent neogenetic trends in the evolution of cortex
in other mammals, the addition of human
language areas would seem to be a most
recent step in a long series of additions. In
this context it is clear that the assumed
ubiquity of neogenetic processes derives in
part from its presumed importance for
human mental evolution.
Additive theories of cortical evolution
The most well known theory of mammalian brain evolution is the triune brain
hypothesis proposed by Paul MacLean (1970,
1973). It is an attempt to explain the difference between mammal brains and nonmammal brains and how this difference
arose in the course of evolution. MacLean
argues that the mammalian brain can be
subdivided into three functionally and evolutionarily distinct regions. The first division, including the spinal cord, brain stem,
midbrain, diencephalon, corpus striatum
and olfactory apparatus are considered a
core structure common to all terrestrial
vertebrates. He calls this the reptilian brain
or R-complex because, he argues, this comprises the entire brain in reptilian species.
During mammalian evolution two additional structural levels are added in
sequence: the paleomammalian brain, composed primarily of limbic cortex and its
associated forebrain nuclei and connections, and the neomammalian brain composed of the neocortex. With the accretion
of each of these systems in the course of
evolution comes the emergence of new
RETHINKING MAMMALIAN BRAIN EVOLUTION
cognitive abilities and behaviors. With the
paleomammalian brain come complex
parental care, vocal communication, play,
and the "higher" emotions of bonding and
caring. With the neomammalian brain
come higher order perceptual, motor and
generally enhanced learning abilities that
free the organism from reliance on fixed
action patterns and simple template based
perception. The scheme is hierarchic and
accretive. The reptilian brain is whole and
complete in and of itself (and presumably
can reassert itself as an autonomous force
in cases of high excitation or weakened
control from higher brain systems, as might
occur with brain damage), and therefore
the addition of new systems does not
require major reorganization, simply
superimposition of new axons into the circuitry of" this otherwise complete system.
The additional connections need merely
play an inhibiting and modulatory role with
respect to the pre-existing substrate.
Although this brain model has become
widespread in the popular literature and
in some psychological and educational theories, its influence in comparative neuroanatomy is tenuous at best. The limbic
cortical areas that presumably comprise the
paleomammalian brain have been homologized to cortical structures in nonmammals by comparative anatomists since early
in the century (e.g., Johnston, 1906; Crosby,
1917; Elliot-Smith, 1919; Dart, 1934;
Abbie, 1940). In addition, more recent
investigations have demonstrated that
nonmammals also exhibit forebrain structures and connections that undoubtedly
have homologues in mammalian neocortex
(e.g., Ebbesson, 1980; Karten and Shimizu,
1989; Ulinski, 1983). It also provides a simplistic view of behavioral differences
between mammals and nonmammals. It
seriously underestimates the considerable
perceptual, motor and learning abilities of
nonmammalian species—particularly birds—
and ignores the many elaborate social
behaviors, modes of communication and
parental care that have been observed in
nonmammals. Nonetheless, the triune
brain theory has enjoyed a wide audience
in large part because it captures a central
anthropocentric intuition: that terminal
661
addition of higher order brain structures
must correlate with the appearance of
increasingly sophisticated cognitive abilities and more flexible behaviors in the
course of evolution.
Ever since it first became possible to easily differentiate one cortical area from
another on the basis of cell architecture or
myelin content it was recognized that the
cortex of some mammals exhibited many
more divisions into distinct areas than did
others. Efforts by the early comparative
anatomists, including Brodmann, Campbell, Elliot-Smith, and others, to determine
homologies between these cortical areas in
different species suggested that apparent
homologies could be identified for a number of areas in most mammalian brains (see
Fig. 8). Apparently, the primary projection
areas for vision, audition, and tactile senses
could be homologized in all mammals but
the multitude of interdigitated "nonprojection areas" that were evident in the
human brain could not all be homologized
to areas in monkey brains, and many of the
"nonprojection areas" in monkey brains
could not be homologized to yet smaller
and more "primitive" brains.
Flechsig's (1901) demonstration that this
precedence of areas was also approximately paralleled by maturational trends
(see Fig. 5) completed the evidence for a
grand synthesis. The evolution of ever
more complex associational abilities in
mammals was enabled by the elaboration
of additional higher order association areas
of cortex that were both progressively further removed from direct peripheral input
and more interconnected with each other.
Just how these new areas of cortex became
interdigitated between old areas was not
clear to these authors, but the determination of homologies appeared to require
the progressive addition of new structures
in a particular order of appearance.
One obvious way to account for new
structure and new functional abilities
appearing in the course of cortical evolution is simply to hypothesize their insertion
into an already complete and functioning
cortex. The prevailing neuropsychological
theory of the early 20th century could
accommodate such a view. Like MacLean's
662
TERRENCE W. DEACON
flattened Owl monkey cortex
electrophysiological
map
flattened mouse cortex
electrophysiological map
rostral
dorsal-medial
dorsal-medial
limbic
rostral
caudal
caudal
B
ventral-lateral
ventral-lateral
rostral
caudal
rostral
dorsal-medial
flattened mouse
cortex cytoarchitecture
caudal
D
ventral-medial
flattened macaque
cortex cytoarchitecture
FIG. 8. Comparisons of the number of cortical areas of small and large mammalian brains. The four drawings
depict unfolded views of mouse and monkey cortical surfaces. A (mouse cortex) and B (owl monkey cortex)
are redrawn from Kaas (1989) and depict area maps determined electrophysiologically in these species. C
(mouse cortex) and D (macaque cortex) are redrawn from Caviness and Frost (1980) and Jouandet et al.
(1989), respectively, and depict cytoarchitectonic divisions (Krieg's numerical designations following Brodmann). The differences in configurations largely represent different "unfolding" techniques, some of which
minimize area distortion at the expense of total map integrity whereas others maintain continuity of the map
at the expense of area distortion. All are drawn to equal size for comparability.
subsequent Triune Brain hypothesis, the
theory of the accretion of new cortical areas
necessitated a hierarchic conception of
brain organization and function. Flechsig
(1900) and Campbell (1905) clearly articulated this complementarity between the
orderly addition of cortical areas in evolution and the progressive elaboration of
sensory motor processes in "higher" mammals. The associationist assumption at the
center of this theory was that higher order
mental associations are built from lower
RETHINKING MAMMALIAN BRAIN EVOLUTION
B
663
D
stem mammal
9
primitive mammal
prosimian
relative
size
Campbell,
Brodmann,
Flechsig
Sanides,
von Bonin
Lende,
Poliakov
FIG. 9. A graphic depiction of Campbell/Brodmann/Flechsig, Bonin/Sanides and Lende/Poliakov scenarios
for the addition or differentiation of new cortical areas in mammalian evolution. Although none of the
individual schemes is exactly identical with any other (and may not exactly correspond with those depicted)
they have been grouped into three distinct categories for depiction because of their underlying theoretical
similarities. The relative sizes of these brains are depicted in column A and show that the increase in distinguishable cortical areas is not independent of size. Column B shows an accretive scheme in which projection
areas are primitive and association areas are added derived characteristics in later brains. Column C shows a
progressive differentiation scheme in which association areas are considered most undifferentiated and therefore primitive and more specialized sensory and motor areas are assumed to be later derived conditions that
have differentiated in a series of stages out of previous levels of association cortex. Sanides additionally argues
that there is a dual origin of most major sensory/motor fields that determines differentiation at the intersection
of a dorsocaudally originating archicortical and a ventrorostrally originating paleocortical trend. This is not
depicted here. Column D shows a progressive differentiation scheme based on the parcellation and retraction
of initially diffuse overlapping projection fields into eventually discrete non-overlapping fields. The boundaries
of the initially overlapping projection fields are depicted by dashed lines. Later parcellation and reduction of
diffuse projections within each projection field is depicted as progressively darker regions of gray. The notion
of reduction of diffuse projections within a field is from Poliakov and is not discussed as a possibility by Lende,
whose theory focused only on the earliest stages of cortical evolution in mammals.
order simpler associations, and complicated and flexible skilled responses are
constructed by associations between simpler reflex responses. They argued that
association areas of cortex only received
information from sensory projection areas
or other association areas and served as the
locus for higher-order associations between
sense data and motor programs from primary areas. Association areas ultimately are
envisioned as an additional higher-order
reflex arc superimposed upon and elaborating existing lower level reflex arcs
(Luria, 1980; Sherrington, 1906). Therefore association areas could be added as
evolutionary after-thoughts with minimal
rewiring of other brain circuits. Each new
association area was one step removed from
the previous association area in the hierarchy and provided an additional layer of
association processes superimposed upon
an already complete functional brain (see
Fig. 9).
Vestiges of this view are still widespread.
The pinnacle of this conception of the cortical hierarchy is of course the addition of
language areas in the human brain—
inserted into an otherwise complete and
functional ape brain. The assumption that
Broca's area for speech is a new association
area peculiar to the human brain has been
represented in a number of schemes by the
identification of some region within the
third frontal convolution of the human
brain that is assigned no homologous counterpart in the monkey brain. The possibil-
664
TERRENCE W. DEACON
ity that Broca's area has no prehuman
homologue and could have been simply
added on to an otherwise complete brain
seems to be taken for granted in a number
of recent discussions of human brain evolution (Passingham, 1981; Tobias, 1981;
Falk, 1983) and language evolution (e.g.,
Chomsky, 1972). Galaburda and Pandya
(1982) and Deacon (1984, 1988a, 1990c)
provide architectonic and tracer evidence
that a homologue for Broca's area exists
in the monkey brain, despite the fact that
it plays no apparent role in vocal communication in monkeys.
A similar argument was presented by
Geschwind (1964) and has been reasserted
by a number of later writers concerning
the inferior parietal lobule of the human
brain. Following the classic associationist
model of language processing, Geschwind
argues that this area plays a fundamental
role as an "association area of association
areas." Its placement at the temporo-parieto-occipital juncture seemed to ideally suit
it for associating the outputs of association
cortices from different sensory modalities.
Since Geschwind conceived of word meanings as complex associations between word
sounds and a multitude of other sensory
associations abstracted from sensory experience, an association area of association
areas would have to be the necessary substrate for semantic processes. Posterior
temporal-parietal-occipital damage often
results in disturbances of semantic language comprehension. Geschwind argued
that no homologue to this highest order
association area was evident in monkey
cortices. According to this logic the addition of this area during human evolution
was a necessary condition for language
evolution. Language evolution becomes the
natural end point of a progressive process
of adding association areas on top of association areas in the course of mammalian
brain evolution. This non-homology claim
has also been contradicted by subsequent
tracer experiments (e.g., Mesulam et al,
1977; Seltzer and Pandya, 1980) and animal behavioral studies (e.g., Jarvis and
Ettlinger, 1977).
Recently, a more sophisticated version
of an accretion theory has been suggested
by Allman (1990) and Kaas (1987, 1989).
They argue that the multiplication of cortical areas in many mammalian lineages
might be explained by the duplication of
existing areas. They suggest that a new
population of neurons could appear interdigitated in position between older populations as a result of some genetic accident
that caused redundant production of the
neurons from one of these regions (it is
compared by Kaas, 1987 and 1989, to the
addition of an extra body segment in the
evolution of lobsters; Gregory, 1935).
Recent studies of genetic mutants in fruit
flies has demonstrated that at least in these
species genetic mutations can cause the
production of whole duplicate body sections or limbs interdigitated between existing structures. Allman (1990) suggests
that similar genetic mutations might
underly areal duplication events in mammalian cortical evolution. In fact, to account
for the number of such events that have
occurred this would have to be a somewhat
common sort of mutation.
It is unquestionable that new cortical
areas have evolved in some of the larger
mammal brains and that different lineages
(e.g., cats and monkeys) exhibit different
spatial arrangements and functional specializations of the cortical areas that have
been added (Kaas, 1989). But is it reasonable to imagine that these areas have been
literally inserted between existing cortical
areas by the addition of new neural tissue
in that position? This claim depends on the
possibility that functional areas of cortex
are modular in their construction. The
neurons that comprise a new cortical area
would need to be added along with specifications regarding intrinsic circuitry and
afferent and efferent connections with
neighboring areas and subcortical sites.
This requirement is contradicted by
developmental evidence that the afferent
and efferent connections of a cortical area
are not specified by the information that
is intrinsic to the cells in that area, but
rather by competitive interactions among
competing axons from many areas. Even
the transplantation of a section of cortex
into a novel position within a fetal brain
prior to the development of cortical con-
RETHINKING MAMMALIAN BRAIN EVOLUTION
nections will not bring with it the functions
and connections appropriate to its site of
origin (O'Leary and Stanfield, 1989; Stanfield and O'Leary, 1985; see discussion in
the next section). The transplanted area
will develop functions and connections
appropriate to its new position. Thus, even
if some new area of cortex miraculously
appeared interdigitated between older cortical areas in a developing brain it would
not bring with it any new connectional or
functional information.
Is there an evolutionary sequence of
new cortical areas?
An alternative approach is to conceive
of new cortical areas as differentiating out
of old areas rather than being created independently adjacent to them. This has the
attractive property that new areas should
continue to bear some functional and connectional interrelationship with their parent areas. Because there is a parent-descendent relationship between areas there will
also be an implicit sequential order of
regional evolution, depending on assumptions about the "initial brain." Correlated
with the order of the proposed evolutionary sequence may also be an implied hierarchy of increasing functional differentiation and complexity.
The comparative cytoarchitectonic studies of Brodmann (1909) and others
appeared to corroborate associationist
assumptions that association cortex was
newer than projection cortex and that some
association areas could only be identified
in the most "advanced" brains. The order
of regional evolution of the cerebral cortex
thus appeared to begin with primary sensory receptive areas and motor output areas
and involve the progressive differentiation
of ever higher association areas. In the initial state sensory and motor areas were
thought to be directly connected to one
another to form direct reflex arcs. With
the differentiation of new areas inserted
between these primary areas the reflex arc
becomes more indirect and reflex action
gives way to more complex and variable
associations between sensation and action.
The more intermediate stages, the more
complex the analytic capabilities and the
665
less driven by reflex and habit. The newer
the area then, the more indirect its link
with the input and the more complex its
functional properties. In this way the differentiation of association areas from association areas could be correlated with an
additive functional hierarchy as well.
It turns out that many of the assumptions
that initially supported this hierarchic
scenario have been subsequently undermined. The first difficulty to crop up was
the discovery that association cortex did
not lack extensive subcortical connections
(Diamond, 1982; LeGros Clark and Northfield, 1939; Rose and Woolsey, 1949).
Association areas cannot be conceived as
added on top of a complete working system, sending and receiving information
only from adjacent lower level cortical
areas. They have independent inputs and
outputs and are thereby completely integrated into the whole brain at every level
every bit as much as are "projection" areas.
More problematic still is the nature of these
connections. The "highest" association
areas exhibit extensive connections with
limbic cortical areas (Pandya and Yeterian,
1985), whose evolution is presumed to predate the evolution of primary projection
areas in all theories of cortical evolution.
Recent tracer studies have also suggested
that association areas project to core midbrain and tectal areas and receive indirect
projections from these areas relayed
through the thalamus. They may even
exhibit connectional topography that corresponds more to midbrain maps than to
cortical sensory or motor maps (Deacon et
al., 1987). These features are also incompatible with the assumption that the "highest" association areas are only connected
with areas at the next hierarchic level down.
In many ways their principal links are to
some of the most primitive brain structures.
Finally, there is the problem of architectonic and functional specialization.
Probably the most extreme architectonic
and functional specializations anywhere in
cortex can be found in the primary visual
and motor areas of anthropoid brains. The
primate striate cortex, for example,
exhibits highly derived cytoarchitecture
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TERRENCE W. DEACON
with distinct sublamination of layer IV,
complex mosaic distribution of different
visual submodalities into a microscopic
matrix of architectonically distinct patches
or "tufts" (Livingstone and Hubel, 1988),
and approximately double the number of
cells per cortical column of any other cortical area in any other mammal (Rockel et
al., 1980). These are clearly recently
derived conditions that indicate a high
degree of functional and architectonic specialization. The architecture of association
areas is generally less variable from area to
area and species to species than is the
cytoarchitecture of specialized primary
areas (Sanides, 1970). This indicates that
structural and functional specialization is
not limited to and probably is less often
exemplified in "higher order" association
areas.
Some of these arguments against the
classic view have also served as support for
an almost exactly inverted view of regional
cortical evolution. Roughly inverted scenarios have been proposed by Bishop
(1959), von Bonin and Bailey (1961) and
Sanides (1969, 1970, 1975). Probably the
most ambitious and most widely adopted
of these is Sanides' theory of progressive
waves of cortical differentiation or "Urtrends." Sanides infers the sequential evolution of cortical areas from a trend toward
increasing architectonic specialization that
culminates in the evolution of specialized
primary projection areas (see Fig. 9). Following the lead of Abbie (1942) and Dart
(1934), Sanides argued that isocortical areas
evolved from undifferentiated periallocortical zones along the borders of primitive hippocampal and olfactory cortex in a
series of stages of increasing differentiation. The trend is envisioned as a series of
"growth rings" (Sanides, 1970) constituted
by progressively more specialized cortical
areas differentiating out of relatively less
differentiated areas. Areas that correspond
to the highest level association cortices in
the classic view comprise some of the most
primitive ancestral areas in this view. The
most highly specialized areas in advanced
brains are envisioned to be the result of
specialized core areas developing within
more generalized areas, and subsequently,
further specialized core areas developing
within these specialized areas, and so on.
The ancient status claimed for association
areas could account for their extensive
connections with limbic structures and their
prominent links to midbrain systems. The
progressive stages of differentiation also
appear to correlate with connectional relationships between cortical areas (Pandya
and Yeterian, 1985). If it is assumed that
cortical areas only retain connections with
their immediate precursors but not second
generation precursors, this model could
explain the links between association areas
and limbic cortex as well as the lack of
connections between limbic areas and
either primary sensory-motor areas or their
adjacent belt areas.
As noted earlier in this discussion, the
most serious argument against Sanides'
evolutionary sequence is the apparent existence of specialized visual, auditory and
somatic projection areas in all mammal
brains, even in marsupials and monotremes (Kaas, 1987). Even the primitive
cortex of turtles exhibits representation of
visual and somatic responses in distinguishable regions crudely appropriate to their
topological position in mammalian cortex.
An ancient status for these specialized sensory areas contradicts Sanides' model.
One possible counter-response is to argue
that the apparent homology between the
specialized primary sensory areas of
advanced brains and the primary sensory
areas of ancestral and conservative brains
is incorrect. For example, von Bonin and
Bailey (1961) argue that the presumed
homology between the hedgehog visual
cortex and monkey primary visual cortex
is not supported by the cytoarchitectonic
criteria Brodmann (1909) originally suggested. They conclude that it is far more
similar to the more generalized areas of
visual association cortex. This interpretation is supported by the fact that in the
visual systems of the opossum and hedgehog the thalamocortical projections of the
lateral geniculate nucleus (corresponding
to primary visual cortex projections) and
pulvinar nuclei (corresponding to extrastriate association cortex projections)
extensively overlap (Diamond et al., 1985;
RETHINKING MAMMALIAN BRAIN EVOLUTION
Kaas et ai, 1970). Although the so-called
projection areas are specialized for receiving relatively more direct information from
peripheral sensors, all adjacent areas comprising a single modality receive independent sensory afferents from the thalamus
and all share some thalamic connections in
common (Caviness and Frost, 1980; Diamond, 1982).
Hierarchic scenarios of cortical evolution are appealing both because of their
agreement with tacit assumptions about
mental progress and mental processes, and
because of the way they simplify the
assumptions about connectional and functional integration associated with the addition of new parts to a complex brain. Adding the new parts to the terminal end of a
growing hierarchy limits the presumed
problems of integration with all lower
levels. It also provides an explanation for
the evolution of complex structures by
demonstrating plausible intermediate steps
in complexity, differentiation and specialization. One serious problem with both
hierarchic schemes for explaining regional
evolution of cortex is the constraint of the
linear sequence itself. Ultimately, both theories are terminal addition theories. The
arguments in support of both are analogous to those for terminal addition in general: addition of new structures to systems
that were complete in an adult of the preceding evolutionary stage; avoiding the
complication of inserting structural
changes in the middle of a complex process; and the assumption that additional
structures augment the function of the preceding structures. As a result they are prone
to similar criticisms. It is not at all clear
that cortical areas within any one brain are
organized according to simple linear hierarchies, nor is it obvious how cortical areas
in different lineages can be homologized
with respect to a strict number of steps in
the evolutionary sequence. For example,
the primate visual system exhibits at least
two, and probably three or four, distinct
processing pathways for different aspects
of visual perception that diverge from
striate cortex into distinct groups of association areas. Like a branching tree structure, one would assume that multiplication
667
of areas should be highest at its terminal
end. If areas are capable of differentiating
out of other areas in the course of evolution there should be an increasing number
of bifurcations as the process continues.
Each sense modality contains multiple association areas at the same level of the cortical hierarchy but only one primary sensory area. This would not be likely if these
specialized areas represented a terminal
end in the evolutionary differentiation process. And yet the extreme other end of the
spectrum—paralimbic association areas—
is not the level of the highest multiplicity
of cortical areas either. In the visual modality it appears that the most diversity and
multiplicity of cortical areas is found at
middle levels in the processing hierarchy.
This is likely true of other modalities as
well. This pattern is implausible in either
of the two general terminal addition scenarios.
Strict hierarchical terminal addition is
not the only possibility, nor is it necessarily
less complex than are non-hierarchic scenarios. Given that most cortical areas are
connected to more than one other area and
that all are connected with distinct subcortical structures, there is no obvious sense
in which a new cortical area can be thought
of as superimposed on an already complete
and functioning system—it must inevitably
emerge in the middle of a complex integrated network. Terminal addition contributes no additional explanatory power
toward solving the problem of the preestablished integration of new areas.
Models of cortical evolution that make
no assumptions about the order of appearance of cortical areas have been outlined
byAllman(1982, 1990) and by Kaas (1987,
1989). They each argue that the multiplication of cortical sensory areas of the visual
system can be explained by duplication of
existing cortical areas followed by subsequent differentiation of function in the new
area. Presumably the new area will initially
share the same connections and cell types
as its older twin and gradually will come to
gain or lose connections and exhibit modifications in cellular architecture associated
with its changes in function. It is often the
case that adjacent cortical areas serving the
668
TERRENCE W. DEACON
same sensory modality also exhibit connections with similar structures elsewhere
in the brain—sometimes to separate divisions of these structures, at other times
overlapping in connectivity, and it is not
unusual that neurons in common afferent
sources will send collateral branches of the
same axon to adjacent cortical areas.
Duplication of this sort would also account
for the many striking homologies between
all isocortical areas.
The duplication of an existing area is
presumed to be a relatively innocuous accidental mutation. However, the availability
of redundant areas frees one of the two
from the constraints of the primary adaptation so that it is able to develop some
additional, complementary visual function.
In the primate visual system it is clearly the
case that distinct visual areas seem to be
specialized for different submodality functions in vision, such as color, form and
movement perception. Thus, by duplication and subsequent differentiation of
function the entire collection of interdependent visual areas could have been produced. The apparently hierarchic arrangement of these areas is not explicitly
explained in either model, but probably it
could be argued that the one area that
retains the ancestral function becomes the
more primary area and the differentiated
one becomes more secondary. Progression
from one to the next in sequence could
then be explained simply on the basis of
the influence of adjacent areas.
Allman (1990) and Kaas (1987, 1989)
assume that new areas are added to an otherwise complete visual system, inserted
between existing cortical areas. This is consistent with their focus on advancement and
augmentation of function as the prime
mover in the evolution of new cortical
areas—duplicated areas become recruited
to some new adaptive function that augments or complements existing functions.
This argument is used to explain how distinct cortical visual areas have become specialized for distinct visual submodalities,
such as color, movement, or form perception. A major criticism is that the separate
functional specializations of the different
visual areas, which Kaas (1989) suggests
might be too complex to be handled efficiently by a single large visual area, do
appear to be handled by only a few visual
areas in small brains. There is no evidence
that "new" functions have evolved, only
that existing functions have become segregated and distributed to parallel visual
processors. If processing all these modalities together in a single area is merely an
efficiency problem, we should expect that
at least some very small brains would also
segregate visual functions into the almost
two dozen visual areas that endow large
primate brains and that at least some large
brains would collapse visual processing into
only one or two visual areas, but this is not
seen. There are clearly size factors involved.
Accretion assumptions are not essential
to explain the appearance of new cortical
areas in mammalian evolution. A scenario
for the early stages of mammalian cortical
evolution was presented by Lende (1963,
1969) that does not make any assumptions
about evolutionary precedence of cortical
areas. He argues that the pattern exhibited
in the common ancestor to all mammals
(including marsupials and monotremes)
included cortical projection fields that were
extensively overlapping and therefore
poorly differentiated from one another. By
a gradual process of differentiation over
the course of evolution each projection field
retracted with respect to the other until in
most modern species each projection field
is exclusive of all others. In Lende's view
the marsupial and basal insectivore cortices
represent a state where the retraction into
separate territories is nearly complete, with
only somatic and motor areas still overlapping. Because it is purely a differentiation
model there is no addition of areas and no
distinction between old and new cortical
fields, just old and new patterns. A closely
related theory of the evolution of connectional differentiation has been proposed by
Ebbesson (1980, 1984) and will be discussed in the next section.
Lende's model was only intended to
explain the earliest stages of mammalian
cortical evolution (better resolution physiological recording techniques have largely
contradicted his claims about the lack of
differentiation in primitive brains; Kaas,
RETHINKING MAMMALIAN BRAIN EVOLUTION
669
isocortical evolution
by areal differentiation
FIG. 10. Depiction of a scheme of progressive differentiation of cortical areas from one another that does
not assume an evolutionary sequence in which some coexisting areas are older than or ancestral to others.
The figures are meant to represent flattened cortical hemispheres with limbic cortical areas representing the
white perimeter of each and isocortical subareas represented by the gray areas contained within. Below each
of the three brains of increasing size is a block diagram of the ancestor-descendent relationship for the
progressive generation of new cortical subdivisions. The use of diverging shades of gray is intended to represent
the differentiation of both descendents of a subdivided ancestral area from the architectonic and functional
characteristics of this ancestor.
1987). However, a simple differentiation
theory suggests some interesting alternative interpretations of area multiplication
problems in more differentiated mammalian brains. In these brains all the projection areas are differentiated from one
another and, with the exception of the
somatic and motor areas, they have also
been separated by interdigitated association areas. The idea of progressive differential retraction of previously diffusely
overlapping projections might account for
differentiation of previously undifferentiated association areas within each sensory
modality. A single sense modality might be
conceived as becoming progressively segregated into differentiated submodalities.
This hypothesis (represented in Fig. 9) does
not necessarily predict that the interdigitated cortex between primary areas should
be any more or less complex than projection areas nor that it should be performing
any higher function.
The assumption that one structure has
to be older or more conserved than another
is actually not even a necessary premise for
hierarchic terminal addition scenarios of
regional cortical evolution. If multiple new
areas result from the differentiation of previously unitary areas it is not necessary that
one of the resulting areas be considered
the homologue of the ancestral area and
the other or others be considered derived.
If we assume either that a new cortical area
results from duplication of an existing area
or that it differentiates out of some sector
of a pre-existing area and eventually takes
on a function that is somehow complementary to that of the other area, then it
should follow that both areas will be
changed in the process.
In this regard, the primary visual cortex
in primates cannot even be strictly homologized to the primary visual cortex in the
squirrel because in the squirrel many of the
visual functions handled in the primary
visual area are in the primate partially distributed to some of its many more numerous nearby visual areas. In a hypothetical
"initial brain" with only one visual area all
the distinct submodality analyses would
have to be performed within that area—
670
TERRENCE W. DEACON
luminosity, movement, color, form, spatial
relationships, local features, etc. There is
no visual area in a brain with many visual
areas that performs all these functions.
Even a duplicate area and its progenitor
should be expected to change with respect
to one another, in the course of subsequent
evolution, so that neither resulting area will
be directly comparable to the original .Thus
it is probably more accurate to view the
multiplication of cortical areas in terms of
progressive differentiation of all areas with
respect to one another. Both areas created
after the subdivision of some previous area
will differ slightly from one another and
from ancestral structure, and these differences will likely increase over time (see Fig.
10). From this perspective it does not matter where in a cortical hierarchy the new
division appears, the differentiation pattern will be essentially the same.
It cannot automatically be assumed that
there is any strict hierarchy from functionally and architectonically simple to complex cortical areas or from phylogenetically old to new cortical areas. A ranking
of areas with respect to relative phylogenetic age or functional complexity is not
unambiguously reflected in neuroanatomical data and may not be consistent with
developmental considerations of cortical
differentiation. Nor are we justified in
assuming that the addition of new functional subdivisions of cortex correlates with
an enhancement of function. The addition
of new cortical areas still needs to be examined with respect to the influence of brain
size. Extensive multiplicity of cortical areas
is never seen in small brains, and the most
extensive multiplicity of areas appears only
in very large brains (e.g., the human brain).
This suggests that areal multiplication
might be the result of facultative developmental responses to brain size and not
distinct genetic adaptations.
Reorganization and the neogenesis of
neural circuits
Whether we explain area multiplication
and functional differentiation in terms of
addition or differentiation there is still a
major problem area that must be addressed:
How are the neural connections of these
areas determined? This rewiring problem
is most troublesome for addition or duplication hypotheses. If an area is completely
new its connections must presumably
invade territories in other structures that
are occupied by projections from pre-existing areas, and it must itself be invaded by
axons from other structures that would
otherwise have found other targets in the
brain. But area duplication does not escape
these problems. A duplicated area may have
all the afferent and efferent specificities
appropriate to the original area but there
still must be overlap in efferent projections
from the duplicate and the original area as
well as a dividing or sharing of afferents.
Any subsequent differentiation of the
duplicated area from its progenitor likely
also involves changes of connections,
including both the loss of many of those
shared with the original area as well as a
shift of its efferents to new targets.
The possibility of connectional reorganization also suggests other options for neogenesis. If connectional organization can
be altered then it is not necessary for new
structure to be added for new functional
areas to emerge, it is only necessary for
their underlying connections to change.
Since ultimately the patterns of connection
determine function within the nervous system, all theories of neogenesis of cortical
structure and function must address this
issue. A theory that fails to explain how
underlying connectional reorganization
takes place is fundamentally incomplete.
The most obvious hypothesis for
explaining the evolution of new connections is that they are simply added. Because
this hypothesis requires axons to enter
novel target areas that are occupied by
other connections and ultimately displace
some of those connections or form new
synapses in that area, it has been called the
invasion hypothesis. The basic features of this
process are diagrammed in Figure 11. Cells
from one area either change their target
or produce collateral branches that invade
a new target area. This idea has had a long
history in comparative neuroanatomy (e.g.,
AriensKappersrta/., 1936; Herrick, 1920)
and seems essential to explain the appearance of certain neural pathways that occur
RETHINKING MAMMALIAN BRAIN EVOLUTION
simple accretion hypothesis
before duplication-addition
after duplication-addition
parcellation hypothesis
diffuse connectivity
before parcellation
differential connectivity
after parcellation
axonal invasion hypothesis
before axonal invasion
after axonal invasion
equivalent cell population hypothesis
before cell migration
after cell migration
FIG. 11. Four commonly cited or assumed rewiring hypotheses are depicted with source nuclei indicated by
shaded ellipses, cortical (or other nuclear) targets indicated by shaded boxes, and connections indicated by
black arrows connecting them. Antecedent and consequent conditions are shown in neighboring boxes. The
simple accretion hypothesis is depicted at the top left, in which a new structure is added to the brain including
its own new connections. The invasion hypothesis is depicted at the top right, in which a new set of connections,
or connections originally targeting some other area, invade and establish synapses in a novel brain structure.
The parcellation hypothesis is depicted at the bottom left, in which previously diffusely interconnected
structures lose some of their diffuse connections in a complementary fashion so as to produce subdivisions of
each that are connectionally distinct. The "equivalent cell" hypothesis is depicted at the bottom right, in
which cells from one region migrate into another structure and attract their afferents to this new structural
position.
in relatively recent lineages but not ances- comparative evidence demonstrates a
tral lineages (Northcutt, 1984). Invasion remarkable conservatism of connection
was long thought to be the explanation of patterns within all vertebrate brains despite
the progressive development of telence- radical differences in size, morphology and
phalic specializations in vertebrate evolu- differentiation. The overwhelming majortion. However, many of the telencephalic ity of major pathways seem to be evident
connections that were formerly thought in all vertebrates and probably represent
absent in anamniotic vertebrates have an inheritance from the common protoverturned out to be demonstrable with axonal tebrate ancestor. Ebbesson argues that
tracing techniques (Ebbesson, 1980; there are no developmental or comparaNorthcutt, 1981). In support of this tive cases that would require the assumphypothesis Northcutt (1984) argues that the tion that axons must have invaded an
presence of a spinothalamic pathway in all unusual target (although his own theory
reptiles, birds and mammals but in no other has been called unfalsifiable in this regard).
group but cartilaginous fishes and the pres- All connection changes in the course of
ence of palliospinal pathways only in birds vertebrate evolution can be explained
and mammals are each best explained by entirely in terms of changes in the relative
invasion.
numbers of preexisting connections—
However, Ebbesson (1980) questions the sometimes complete loss of connections—
plausibility of invasion on the grounds that in prior lineages. Ebbesson (1980) refers
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TERRENCE W. DEACON
to this as the parcellation theory of brain evolution. Invasion, he argues, either simply
does not occur or else is extremely rare.
A schematic depiction of the parcellation hypothesis is presented in Figure 11.
Parcellation theory presumes that the
ancestral condition consists of a relatively
undifferentiated pattern of connections
between two given structures. It is proposed that progressive loss or retraction of
a selected subpopulation of these fibers is
responsible for subsequent differentiation
of each ancestral structure into two, connectionally and functionally distinct subdivisions. In the course of time further parcellations of these separate projection
systems can result in yet further functional
specialization. This process would account
for increasing multiplication of areas and
increased differentiation of functions in the
course of brain evolution. It also explicitly
predicts many details of connectional organization that should correlate with areal
multiplication and differentiation in cortex. Because subdivided areas originate
from a single area they each will inherit
most but not all of the connections of that
ancestral area. For example if an extrastriate visual area in some ancestral lineage
becomes subdivided in its descendents we
should expect that the lateral posterior
(pulvinar) thalamic source of the ancestral
afferents would be the same for the descendent subareas although perhaps subdivided into new subdivisions, and we should
expect that the tectal targets of its efferents
should likewise be the same structure or
some new subdivisions of that structure.
These predictions appear to be reflected
in the organization of afferent and efferent
connections of brains with relatively few
visual areas as compared to those with many
visual areas (Diamondsa/., 1985; Kaasand
Huerta, 1988), as well as in other systems.
Ebbesson further argues that the evolutionary history of parcellation events is
recapitulated in developmental processes.
Parcellation must occur at some particular
stage in the development of existing connections in any organism and subsequent
further parcellation processes must be preceded by this first parcellation. Following
von Baer's principle of character prece-
dence {i.e., that members of two or more
closely related taxa will follow the same
course of development to the stage of their
divergence), this would mean that two
species' developmental patterns would
coincide to the point of the first parcellation event that distinguished them, and
therefore that parcellation events that follow others in evolutionary time, and further parcel the same projection system,
should also be expected to occur subsequent to the first in development. This predicts that early stages of brain development
should be characterized by the presence of
diffuse axonal projections and later stages
would proceed through progressive parcellation processes.
In general terms this pattern of progressive culling of initially diffuse connections is indeed exhibited in brain development. Initial axonal projections appear
to be less selective and more exuberant than
the projections that survive to adulthood,
and as a result fetal axons contact many
more targets and a much wider variety of
targets than do adult axons. During subsequent development these superfluous
connections are culled, resulting in connections that are far more specific and
topographically organized (Jacobson, 1978;
Purves and Lichtman, 1980, 1985). The
details of this process will be discussed in
the next section.
Ebbesson (1980) argues that this developmental pattern recapitulates the ancestral sequence of differentiation of each
brain area and its connections. These transient connections within the fetal brain are
therefore viewed as "fossils" of earlier patterns of brain organization. Viewing certain transient events in neural development as "fossils" may also help explain the
appearance and subsequent elimination of
whole classes of neurons during development that serve as transitory targets for
projections ultimately destined for other
targets. However, this strictly recapitulationist interpretation is not critical to the
parcellation hypothesis and weakens its
generality and predictive power. Although
Ebbesson's (1980) initial examples compare the adult organization of connections
in primitive fish to embryonic connections
RETHINKING MAMMALIAN BRAIN EVOLUTION
in terrestrial vertebrates (and therefore
have earned the criticism that it assumes a
crude scala naturae view of living species
as well as strict terminal addition), the
underlying assumptions of parcellation
theory do not require terminal modification. The relative timing of fetal parcellation events need not be an exact recapitulation of evolutionary events and the
hierarchic dependency of one parcellation
event on another during development does
not necessarily imply a corresponding phylogenetic order as well.
A more flexible version of the theory can
be articulated if we assume that a new parcellation process can affect connections at
any stage of brain development during the
course of evolution. Parcellation processes
that alter connections early in ontogeny
will operate on less differentiated connection patterns than parcellation processes
that occur later in ontogeny. Presumable
parcellation processes during development
depend on earlier parcellation processes,
and so a subtle change at an early stage
might have radical consequences for adult
structure. A wider range of reorganizational effects should result from mutations
that influence parcellation early in development, particularly if subsequent parcellation events are undermined or biased by
the earlier changes. But if changes in parcellation processes can occur at any developmental stage, then brain development
cannot be a strict recapitulation of the order
of phyletic events in brain evolution. A very
early change may have been very recently
acquired whereas a relatively late parcellation change may date to a much earlier
evolutionary epoch. Recently acquired
parcellation changes that affect an early
stage of development could fundamentally
alter the pattern of all subsequent developmental events so that they no longer
reflect any meaningful phyletic series.
The parcellation hypothesis is the inverse
of the invasion hypothesis both in its mechanism and in the assumptions that it makes
about ancestry-descent relationships of
brain structure. In more general terms, the
invasion hypothesis is an example of an
additive theory and the parcellation
hypothesis is an example of a differentia-
673
tion theory. Each of these two mechanisms
for connectional change correspondingly
supports additive versus differentiational
theories of brain structure evolution. The
parcellation hypothesis is most consistent
with area addition scenarios involving differentiation of new areas from old areas
and is inconsistent with simple addition
hypotheses that presume the invasion of
new axons. It is probably also inconsistent
with area duplication scenarios, since it
would reject arguments about projectionmap duplication as invasion hypotheses.
But in addition, parcellation is also
inconsistent with many of the existing differentiation theories of cortical evolution
because it does not predict that ancestral
cortical areas can remain unchanged as new
areas differentiate out of them. For this to
happen the older of the two areas would
have to maintain all of its previous connections and the newer area would be distinguished only by its lack of certain connections and not by the possession of
connections not also present in the older
area. Considering either thalamic afferents
or subcortical efferents, it is clearly not the
case that there are cortical areas that possess all the connections of any of their
neighbors; each has slightly different
although often partially overlapping afferents and efferents. The adjacency of afferent sources and efferent targets of neighboring cortical areas as well as the partial
overlap in some of these is strong support
for the parcellation hypothesis.
Invasion is a necessary hypothesis for
both additive and recapitulational theories
of regional cortical evolution. Structural
addition is doubly challenged by the parcellation hypothesis because the latter denies both the possibility that new structures
can arise from nothing and the possibility
of invasion of new structures by axons that
did not contact it ancestrally. Even though
they do not assume structural addition,
sequential differentiation theories, such as
the traditional evolutionary hierarchy from
projection to association areas or Sanides'
inverted hierarchy from association areas
to specialized sensory and motor core areas,
are also both tied to the assumption that
axon invasion events have been common-
674
TERRENCE W. DEACON
place. If Ebbesson is correct in assuming
that these are rare events in evolution then
all of these hypotheses must be abandoned.
It is important to recognize that the parcellation hypothesis is a stronger claim
about what can and cannot happen in brain
evolution. Invasion hypotheses do not deny
the possibility that parcellation may play a
major role in brain evolution. Invasion is
an additional assumption beyond parcellation. One difficulty with denying any possibility of invasion is that to do so often
requires hypothesizing many more connectional changes in order to explain a
phylogenetic difference that can otherwise
be explained by only one or two invasion
events (Northcutt, 1984). Although Ebbesson's argument for the ancient status of
preset axon-target affinities is also corroborated by evidence of the ancient and
highly conservative nature of molecular cell
communication and recognition mechanisms (Fasolo and Malacarne, 1988), such
specificity may be determined by complex
relationships of timing and hierarchically
organized interactive effects that render
this specificity somewhat degenerate (Edelman, 1987).
A more fundamental criticism of parcellation theory in its strong form is that
it is preformationist (see numerous commentaries following Ebbesson, 1984).
Either some initial protovertebrate brain
(possibly long antedating the vertebrates)
must be considered to have been completely undifferentiated in connectivity and
therefore totipotential from an evolutionary standpoint, or some of the initial connection patterns must have been preformed from the start and require
explanation in some other way, e.g., invasion. The first possibility is more consistent
with the exclusivity assumption of parcellation theory.
There is no stage of brain development
that exhibits a corresponding totally interconnected undifferentiated state. Although
there is some degree of initial overproduction of projections and poor differentiation
of target sites in the fetal brain these connections are neither totipotent nor completely undifferentiated. Most pairs of brain
structures never pass through a stage where
they are connected. Target sites are underdetermined but probably not undetermined by genetically pre-established
molecular affinities between growing axons
and potential target cell substrates. During
embryogenesis axons do in fact grow out
from their cells of origin to invade different structures distantly located in the brain.
En route to their genetically underdetermined target zones they also may pass
through structures with which they establish no synaptic contacts or only transient
contacts. Both the active "exploratory"
nature of embryonic axons and the prespecification of initial target zones are
potentially troublesome for parcellation
theory. However, permanent loss of many
connectional affinities over the course of
vertebrate evolution is entirely consistent
with the theory.
Both the invasion hypothesis and the
parcellation hypothesis can probably be
used to explain almost any possible
arrangement of connections in the living
vertebrates. The invasion hypothesis makes
many more assumptions regarding each
individual rewiring event, whereas the parcellation hypothesis makes some other
rather troubling assumptions about the initial condition of vertebrates and often
requires considerable theoretical circumlocution to explain what could be explained
by a single invasion event. Each handles
certain examples better than the other.
A third alternative hypothesis, that to
some extent can serve as a bridge between
these two polar opposites, is the equivalent
cell hypothesis suggested by Karten (1969;
Nauta and Karten, 1970). On the assumption that the connectional relationship
between two different cell groups within
the brain is specified by some features specific to those cells, their connection should
be maintained even if one of these cell populations became displaced or actively
migrates to an entirely different location.
Karten (1969; Karten and Shimuzu, 1989)
proposes this as a possible explanation for
the apparent homologies between thalamic
projections to the dorsal ventricular ridge
in birds and to the isocortex in mammals.
He argues that cells which in avian and
reptilian brains are destined to form the
RETHINKING MAMMALIAN BRAIN EVOLUTION
dorsal ventricular ridge may take a different migratory pathway in mammals and
ended up in cortex. In the process they
would also attract their thalamic afferents
to this new target site. The general form
of the equivalent cell hypothesis is schematically diagrammed in Figure 11. With
respect to mammalian cortical evolution it
has the advantage of also accounting for
the significantly increased number of cells
that occupy mammalian as compared to
bird and reptile cortex. This hypothesis
could thus explain the apparent "invasion"
of new thalamic afferents into the cerebral
cortex and also the apparent "invasion" of
new subcortical targets by cerebral cortical
cells without requiring the appearance of
any new connections or any change in the
underlying topology of connection patterns. However, the theory makes strong
assumptions about the specificity of developing connections that may limit its generality.
Increasing the amount of comparative
data will not by itself be able to help us
choose between these alternative hypotheses. The homological ambiguities limit our
ability to clearly discern what is a new connection and what is a modified ancestral
connection. And since each of the different
hypotheses are sufficiently flexible to
account for nearly any pattern, we have
only parsimony assumptions to rely upon.
Unfortunately, the capricious nature of the
evolutionary process suggests that we
should not place too much confidence in
parsimony arguments. Each of the alternative theories proposed to account for
connectional reorganization depends on
specific assumptions about the ontogenetic
processes that ultimately determine connectional specificity. We can thus turn to
the ontogenetic data to look for developmental constraints that can rule out certain
versions of these hypotheses and developmental patterns that might suggest alternative mechanisms.
ONTOGENY CONSTRAINS PHYLOGENY
Ontogeny of neural populations,
connections and functional areas
There are clear trends in mammalian
brain evolution. However, the evidence for
675
these trends and the theoretical assumptions about homologies, progression, size
increase and cortical neogenesis have been
shown to be seriously flawed. Although
patterns are evident it cannot be decided
on the basis of comparative evidence
whether they are merely secondary architectonic adaptations to differences in brain
size, adaptations for sensory-motor specialization, or independent macroevolutionary tendencies for increased cognitive complexity and diversity. However, the
predictable relationships between these
trends and differences in brain size as well
as the parallelisms exhibited in distant lineages suggests that many of these patterns
may instead be the result of underlying
developmental homologies that are
expressed differentially with respect to
brain size or certain sensory-motor specializations (e.g., regression of vision in
echolocating bats).
A considerable body of new data is
emerging concerning the development of
the brain that can help sort out which evolutionary hypotheses are tenable and which
are not. Obviously brains do not evolve
from one adult form to another, although
this is often how brain evolution is portrayed. In fact, most of the well established
theories of cortical evolution make no
assumptions at all about brain development processes (except possibly the
erroneous assumption that they should recapitulate phylogenetic trends). But phylogenetic change in brain structure is the
result of changes in the process of brain
development and ultimately must be
explained in terms of developmental
mechanisms. The importance of ontogeny
to the understanding of brain evolution is
not that it recapitulates phylogeny—it
almost certainly does not—but that it constrains the possible modes of variation that
phylogenetic changes can exhibit (Alberch,
1982; Katz, 1982; Smith-Gill, 1983). If
there are only certain ways that cell populations, functional areas or connections
can develop within a brain, then patterns
of phylogenetic change will tend to be limited accordingly and the possibility of parallel evolutionary trends arising in independent lineages will be increased.
676
TERRENCE W. DEACON
one area of a donor's cortex and transplanted to a different area of a recipient's
cortex (Fig. 12). In the mature brain the
transplanted cortical tissue takes on the
sensory or motor functions, assumes the
cytoarchitecture and even develops the
appropriate afferent and efferent connections characteristic of its new cortical position rather than its place of origin (O'Leary
and Stanfield, 1989; Stanfield and O'Leary,
1985). This relatively late, interactive
determination of cortical structure and
mature
connections
function has very significant implications
for the evolution of isocortical subdivisions. The evolution of a new cortical
region must therefore be a systemic proFIG. 12. Fetal cortical transplant experiment in rats
in which either a frontal cortical sector is transplanted
cess and not the result of the isolated local
to the posterior cortex of a host or a posterior cortical
expression of genetic mutations.
sector is translated to the anterior cortex of a host.
In either case, when the animal matures it is found
The structural and functional differenthat the cortical efferents from these transplants proj- tiation of any cortical area is thus not specect to the appropriate targets for their new position
ified by the specific local cell lineages that
rather than the target specified by their original positions (i.e., rostral sensory/motor areas send efferents constitute it, and takes place considerably
after neurogenesis has been completed for
to the brainstem and spinal cord whereas posterior
visual areas send efferents to the tectum).
all cortical areas. The determination of
what constitutes a distinct functional area,
where its boundaries are, how big it is with
A number of features of the neural respect to neighboring areas, the local spedevelopmental process in mammals are cializations of its myelo- and cyto-architecincompatible with scenarios of cortical evo- ture and its connections with other areas
lution which assume that cortical areas can are all largely independent of which cells
appear, differentiate, or even change their constitute that area. New cortical areas that
relative sizes as independent units during appear in the course of evolution cannot
the course of evolution. This is because the have been added as whole units correinformation that specifies the size, archi- sponding to specific populations of new
tectonic organization, afferent and effer- cells.
ent connections and therefore the basic
Classic theories of additive cortical evofunction of a cortical area during its devel- lution are clearly inconsistent with this
opment is determined by factors extrinsic constraint of cortical development because
to the cells that comprise that area. The it undermines any possibility of discrete
neurons that comprise the fetal cortex are terminal addition. It also undermines addinearly all produced prior to the stage at tive theories of differential area expansion
which the cortex is invaded by thalamic as well (e.g., differential addition of cells to
afferents and prior to the stage at which association cortex with respect to primary
axons originating from cortical neurons cortex during cortical advancement). The
reach their targets. At this stage the cell addition of cells to one sector of cortex
groups in different positions on the cortical rather than another does not determine
mantle are effectively totipotent with which of the cells will or will not be included
respect to their ability to assume any of the within the functional regions that develop
different functional roles exhibited by cell in that sector. The enlargement of the corgroups in the adult cortex (O'Leary, 1989). tex cannot be thought of in piecemeal
The most striking evidence for this comes terms. Addition of new cortical areas canfrom fetal transplantation studies where not be the direct cause of the addition of
sectors of fetal cortex are removed from new brain mass. Given the fact that there
fetal cortical transplants
RETHINKING MAMMALIAN BRAIN EVOLUTION
is no specific topographic information represented in the developing cortex, cortical
expansion must be considered as a whole
and area by area size determination must
be a consequence of secondary processes.
The expansion of the total cortical mantle is probably not determined by a simple
increase in neurogenesis either. The target
size of the cerebral cortex is likely determined prior to neurogenesis for the structure as a whole. During neurogenesis precurser cells in a germinal layer deep to the
developing cortex divide to produce neurons that leave this zone and migrate along
the length of special radial glial guide cells
that extend from the germinal layer to the
surface of cortex. These guides effectively
limit tangential migration (although see
Walsh and Cepko, 1988, for discussion of
exceptions) and serve to align succeeding
cells along this radial column occupying
ever more superficial positions. Radial
guides and the germinal zone at their base
probably form distinct proliferative units,
or ontogenetic columns (Rakic, 1988). All
cell types within that columnar unit of cortex are derived from a common polyclonal
precurser. Earlier it was noted that the
number of cells within a column of cortex
of the same tangential dimensions is
approximately the same in species with very
different size brains (Rockel et al., 1980).
This indicates that neurogenesis within an
ontogenetic columnar unit is invariable
across species and irrespective of brain size.
Cortical expansion must therefore be
understood in terms of the addition of more
ontogenetic columns as opposed to the
increase of cell production within these
columns (Rakic, 1988). Since the multiplication or germinal precursors which will
establish ontogenetic columns occurs prior
to the terminal differentiation of neurons
from these precursors, the determination
of the size of the cortical mantle must be
determined prior to neurogenesis within
the cortex.
The determination of area dimensions
and boundaries within the developing cortex must occur subsequent to neurogenesis
by virtue of other mechanisms. This
appears largely to be the result of afferent
and efferent interactions. In the discussion
677
of parcellation theory it was noted that the
first axonal connections during development are over-exuberant and relatively
unselective. These initially diffuse projecting cortical afferents and efferents compete for dwindling synapses within their
target areas. This competition results in a
significant culling of axons and axon collaterals and some cell death. The relative
correlation of the neural activity of an axon
with others in the near vicinity which relay
similar information to an area is thought
to play a significant role in determining
which axons will be retained and which will
be eliminated (Purves and Lichtman, 1985).
The resultant parcellation of connections
largely determines which structural and
functional characteristics will develop.
This is demonstrated dramatically in the
case of cortical efferents. In an infant rat,
cells in layer V of all regions of the isocortex appear to give rise to axons that extend
into the spinal cord, but in the adult rat
only rostrally located somatomotor areas
contain cells with spinal projections. More
caudally located areas, specialized for visual
or auditory modalities, lose these spinal
connections but retain connections to the
tectum (see Fig. 13). This explains why heterotopic cortical transplants take on the
connections and functions appropriate to
their new cortical position. A similar pattern of overexuberant and relatively undifferentiated connections followed by later
culling of a large number of these connections has been extensively documented for
thalamocortical projections and for corticocortical connections, among many other
systems (see reviews in Jacobson, 1978;
Purves and Lichtman, 1980, 1985; Purves,
1988).
Connectional interactions must also play
the major role in determining the size that
a source or target structure will attain. The
elimination or reduction of afferent connections to a cortical area during fetal
development can cause it to be reduced in
size, can cause neighboring areas to become enlarged, and can cause a corresponding displacement of the cortical
boundary between them (Rakic, 1988).
Deafferentation does not seem to diminish
the numbers of cells per cortical column
678
initial axon
migration
TERRENCE W. DEACON
nections. It appears that alternative projections inevitably will substitute for the
lost afferents. However, some degree of
cell death—particularly at early stages—
may play a role in cortical parcellation and
distinguishes certain cortical areas from
others (Finlay and Slattery, 1983).
The displacement hypothesis
An alternative general model of connectional reorganization processes that takes
into account both allometric effects and
the competitive parcellation process that
sculpt cortical areas can be derived from
these developmental considerations. As a
result of investigating different problems
in comparative neuroanatomy, Deacon
mature
(1984, 19886), Finlay et al. (1987), Purves
connections
(1988) and Wilczynski (1984) have each
suggested that ontogenetic factors play a
central role in the reorganization of neural
FIG. 13. Exuberant efferent cortical projections and circuits in response to differences in neural
culling of connections in development is depicted with populations, regressive processes and perrespect to frontal somatomotor areas and posterior turbations of maturation schedules, or
visual areas. Early in the development of an infant rat homoplaseous changes in peripheral senpyramidal cells from layer V of nearly all cortical
regions have axons that reach these targets. These sory or motor structures. These views are
exuberant axons are later culled in a parcellation pro- similar enough to be capable of synthesis
cess driven by dynamic interactions between com- into a single general model of the strucpeting axons and their targets.
tural reorganization processes underlying
most brain evolution.
in that area, only the number of columns
The displacement hypothesis, as it can be
that are contained within a projection area. called, argues that loss of connections,
In other regions of the brain, more limited acquisition of additional connections or
in their possible sources of afferent input replacement of one class of connections by
or efferent targets, a loss of connections another occurs when competitive axonal
can induce significant cell death. This interactions are biased by contextual events
appears to be the case with many periph- during development. This can happen as a
eral afferent targets (e.g., the lateral genic- result of changes in relative size relationulate nucleus, Rakic, 1988) and efferent ships (an extreme example of which might
sources (e.g., brain stem motor nuclei, Alley, be complete cell death for a particular tar1974; Sohal, 1976). This cell death is pre- get or source), changes in the amount or
sumed to play a role in the functional intercorrelation of afferent information to
matching of peripheral afferents to target one system as opposed to another, or
neuronal populations without the need for changes in the relative importance of inigenetic changes in order to specifically tial axon-target affinities, or changes in
match afferents and cell populations in developmental timing. Four possible modes
every instance of size change in evolution of connectional displacement are depicted
or of peripheral homoplasy (Cowan, 1973; in Figure 14. Although the figure depicts
Cowanria/., 1984; Finlay et al., 1987; Wil- size relationships, this can be understood
czynski, 1984). The multipotentiality of metaphorically to represent competitive
cortical cells both for afferent and efferent biases of all kinds. For example, synchroconnections probably accounts for the lack nization of target cell maturation and
of significant cell death due to loss of con- axonal arrival would increase the likeliimmature
connections
679
RETHINKING MAMMALIAN BRAIN EVOLUTION
axonal connectivity after
efferent target expansion
axonal displacement
hypothesis
axonal connectivity after
efferent target reduction
m
ancestral condition
As
1
1
1
axonal connectivity
before allometric
reorganization
(
>
axonat connectivity after
afferent source expansion
1
1
1
6
4
N
axonal connectivity after
afferent source reduction
FIG. 14. The displacement hypothesis is depicted with four possible interpretations of invasion-like effects.
In each case either the effective enlargement or reduction of targets or afferents (depicted by the size of the
structure) is invoked to explain the source of bias driving the displacement of connections from one target
to another. An analogous pattern could be produced by relative increases or decreases of axon-target affinities
or by heterochronic advantages of some afferents over others that mature at different times. These could
also be depicted in this manner by assuming that the relative size of the structures depicted represents afferenttarget biases in general. Although displacement can also explain parcellation processes, these are not depicted
here. They would roughly follow Ebbesson's schema with the added provision that parcellation is not spontaneous, but must be induced by a change in the relative numbers of target synapses with respect to competing
axons or changes in axon-target affinities. Even if enlargement of both afferent and target populations during
generalized size increase is isometric, there may be limited collateral extent of correlated axon activity such
that diffuse overlap of connectivity could not be maintained and axons from the same source would have a
better chance of eliminating interdigitate axons from more diverse origins.
hood of synapse formation with respect to
axons arriving out of synch with cell maturation. As a result heterochronous
changes in developmental time schedules
for different systems may be a source of
developmental bias analogous to differences in size of competing projections. The
increased affinity for synchronously arriving axons should have the same effect as
relative enlargement of one source or target area with respect to another.
Both invasion-like and parcellation-like
processes are explainable in this way. What
is different about displacement hypotheses
is that they propose that all such events are
driven by competitive biases between different axon populations and their prospective targets and not by instructional
processes such as might be encoded in
molecular affinities. The strong form of
the displacement hypothesis denies both
the possibility of spontaneous axon invasion and also the possibility of spontaneous
parcellation. But like the parcellation
hypothesis it assumes that the basic molecular affinities between initial connections
and their targets are essentially conservative, and if anything, only change in
response to prior displacement events,
under selection to stabilize a newly adaptive circuit against the regressive influences of competing biases. What is missing
from both invasion and parcellation theories is a cause. Displacement theories
introduce cause in the form of regressive
processes (e.g., cell death or reduction of a
peripheral sensory or motor system) or differential growth processes (e.g., unequal or
hyperplasic neuron production, expansion
of some peripheral organ, or heterochronic change in maturation schedules for
different structures).
Finlay et al. (1987) suggest that regressive events during development, such as
cell death and axon retraction, may account
for total brain size variation, the elaboration of specialized sensory, motor or cognitive adaptations, and allometric dispro-
680
TERRENCE W. DEACON
portions of specific systems during brain
evolution. Widespread cell death appears
to be a normal developmental mechanism
for sculpting cell populations of interconnected structures. To a limited extent cell
death may be exaggerated or eliminated
by variations in afferent populations or
efferent associations. These effects are,
however, buffered in systems with multiple
afferent sources and efferent targets, and
so can be expected to be most significant
in systems with highly limited connectional
relationships. Neural populations of
peripheral sensory and motor projections
are generally entirely dependent on
peripheral structures as afferent sources or
efferent targets, respectively, and provide
the most notable examples of variation in
cell death.
A number of the changes in CNS organization in response to the evolution of
novel sensory organs or motor systems may
thus be the result of such a sculpting process. Wilczynski (1984) reviews evidence
for the neural reorganization of CNS cell
populations and connections in response to
some major vertebrate sensory and motor
specializations (e.g., auditory, electrical and
infrared reception) that show relatively
subtle differences centrally in response to
major changes of the periphery. Despite
homoplaseous peripheral changes, central
reorganization often recruits homologous
systems for similar perceptual processes.
He argues that the interlocking of peripheral and central reorganization in these
cases arises out of competitive developmental processes that match peripheral
functional requirements to central functional predispositions and match cell populations to one another. Although there
may be major changes in cell number in
peripherally specialized nuclei as a consequence of cell death there appear to be no
"cascading" effects on cell death throughout the remainder of their functional connections within the CNS.
The main point of the cell death hypothesis proposed by Finlay et al. (1987) is to
account for quantitative allometric changes
in the brain and brain structures. However, there are a number of reasons why
cell death is unlikely to be a significant fac-
tor in major allometric changes. First, in
order to play a significant role it must be
able to account for at least a major part of
the many thousandfold differences in brain
size. Small brains are simply not analogous
to large brains that have experienced 99%
cell death. The role of cell death is clearly
limited to secondary "fine tuning" of independently developed functionally interdependent systems (although it may reach
80% in peripheral receptors). Second, as
compared to peripheral systems, the evidence suggests that the total amount of cell
death is relatively small in most forebrain
structures, even if peripheral structures
relaying information to them are significantly reduced (Rakic, 1988). This probably correlates with the fact that forebrain
structures receive afferents from and send
efferents to diverse cortical and subcortical
structures rather than just one, as in
peripheral structures. The cell death
reported in areas like cortex appears to be
associated with cells maintaining transient
synapses during early phases of development that may serve a preliminary organizing role for later stages. If there was significant cell death in the normal
development of cerebral cortex it would
have to be relatively uniform because of
the remarkable uniformity of cell numbers
per area in all areas and all species. The
initial production of neurons (or the initial
production of "ontogenetic units" with
fixed neuron production patterns) is probably much more important in determining
populations in most structures.
Finlay et al. (1987) also point out the possible significance of heterochronous maturational processes for both cell death and
connectivity patterns. They argue that earlier maturation or delayed maturation of
areas may introduce competitive biases in
normal axonal competition. Since some
competitive processes may extend for only
a few days, significantly delayed or premature connections may be left out of the
competition, with cell death or connectional replacement resulting. Although
Gould (1977) argues for the widespread
presence of heterochrony in other systems
(e.g., somatic growth and puberty) there is
little evidence concerning variance of mat-
RETHINKING MAMMALIAN BRAIN EVOLUTION
uration schedules in the mammalian nervous system at this time. However, time
scale effects may be significant in mammalian brain evolution. The maturation of
a small mammal brain may be completed
within the space of weeks whereas that of
a large brain may take many years. This
means that the absolute time scale of competitive-regressive events during maturation can differ enormously despite the likelihood that at the synaptic and cellular scale
the trophic processes that underly these
effects are the same for all mammal brains.
The prolongation of these events in larger
species might affect variability, degree of
differentiation or sensitivity to extrinsic
influences. In non-mammalian vertebrates
where neurogenesis may persist throughout the lifespan heterochrony may be a
more significant factor.
In previous papers I have also proposed
that axonal competition and other regressive processes play crucial roles in brain
evolution (Deacon, 1984, 19886, 1990c),
but I have focused largely on the possible
influences of size relationships. If the
determination of initial cell number in most
structures takes place prior to major axonal
invasions, the major role of competitive and
regressive processes must be the subdivision of these neurogenetic fields with
respect to each other. Even though no cell
death nor substantial cell saving may result
from increases or decreases of specific
afferents or efferent targets of a multiply
connected structure within the CNS, such
changes can substantially alter local axonal
competition processes. Rather than axonal
competition determining the size of brain
structures via cell death (probably only significant for peripheral structures), the relative sizes of interconnected brain structures should be a major determinant of
patterns of axonal connection.
Allometric effects are probably the most
common sources of bias, given the enormous range of brain sizes and the great
ranges in the relationships between central
and peripheral systems. These effects are
not limited to unusual reorganization
events. Deviations from isometry with phyletic size increase is the rule among brain
structures as in peripheral organs (e.g.,
681
Armstrong, 1985; Campos and Welker,
1976; Deacon, 19886; Gould, 1975; Passingham, 1975; Sacher, 1970; Stephan,
1969). The systematic differentials in neuronal production in different structures in
brains of different sizes should determine
correlated differences in structural parcellation throughout. For example, the regular increase in proportion of visual association cortex with respect to visual
koniocortex in brains of increasing size may
reflect a growing competitive disadvantage
for primary projections in the recruitment
of cortical space determined by a growing
disproportion between the retina and its
potential thalamic and cortical targets.
As we examine species differences in
neural organization we should expect to
see certain necessary correlations between
changed connection patterns and the allometries of the various structures involved.
For example, in cases where an invasion
event is suspected to have taken place one
would expect to find some corresponding
deafferentation of the new target by a former projection source that has regressed
in size with respect to its target, or some
unusual size increase in the new source
structure relative to its normal target, or
significant regression of its normal target.
In cases where loss of connection is suspected either cell death in the source or
target or, alternatively, displacement by a
projection from a disproportionate competing afferent source would be expected.
Failure to find these correlates either in
adult brains or during development would
falsify a displacement interpretation.
Displacement hypotheses are falsifiable
in ways that parcellation or invasion
hypotheses are not because a displacement
explanation is an account of a mechanism
not merely of a change in structure. The
displacement hypothesis is essentially an
extension of well studied mechanisms of
developmental axonal plasticity. The production of topographic functional and connectional organization within a developing
area induced by reduction or over-exaggeration of input from some outside source
is the limiting case for developmental displacement. The extension of this concept
to incorporate allometric influences as a
682
TERRENCE W. DEACON
major source of bias on major projection
patterns completes the synthesis of allometric effects, neogenetic processes and
developmental processes.
Displacement interactions can also conceivably account for true invasions of axons
into targets that even exuberant projections would not otherwise contact. It is not
necessary to assume any changes in the
actual affinities of axons for their targets,
only the reduction of the specificity
requirements for target affinity. This may
occur under some extreme conditions. In
an earlier section it was noted that the initial target specificity of many neural connections is significantly underdetermined.
This has been best documented for cortical
afferents and efferents but has also been
noted widely in the developing nervous system. As a result, initial axonal projections
invade a multitude of diffuse targets and
may establish numerous transient synapses
that will later be eliminated. There probably are some predetermined affinity gradients involved because these initial projections are far from entirely random.
Edelman (1987) has argued that this initial
affinity between axons and potential target
cells is the result of specific cell surface
molecules that exhibit a range of interaction or "recognition" strengths (analogous
to immunological binding relationships).
In order to produce distinct connections
these affinities need not be highly specific
so long as there is either a significant
threshold difference between nearby
potentially competing projections or a
means for dynamic parcellation of relatively nonspecific projections, as is found
in cortex. Extremely weak axon-target
affinities can likely only exhibit themselves
when all competing affinities are essentially
eliminated or when extremely strong competitive biases are introduced. Elimination
of alternative stronger affinity competitors
can occur if the majority of normally
occurring transient and permanent afferents to an area are eliminated, or if a normal target is essentially eliminated, forcing
axons to compete for alternative low affinity targets. Strong biasing may also occur
if a weak affinity afferent source becomes
disproportionately large with respect to
both its normal target and nearby low affinity targets.
An experimental example demonstrates
this possibility. Frost and Metin (1985) and
Sur et al. (1988) have demonstrated the
possibility of inducing optic afferents to
project to inappropriate thalamic nuclei
and thus relay inappropriate sensory information to their cortical target areas. To
accomplish this in a developing rat they
destroyed all the normal targets of the optic
projections (including lateral geniculate,
superior colliculus and visual cortex) and
additionally deafferented another thalamic nucleus {e.g., either the ventrobasal
or the medial geniculate nucleus) by cutting ascending (spinothalamic or tectothalamic) fibers. One of these procedures
is diagrammed in Figure 15. Despite the
fact that the misrouted connections innervate anomalous thalamic targets which
project to non-visual cortical areas, cells in
these areas exhibited response properties
appropriate to visual cortex. This demonstrates that fundamental rewiring is
achievable by displacement and that the
new connections thereby established can
differentiate their targets appropriate to
their new functions. However, it may be
significant in these cases that the alternate
thalamic and cortical targets are homologous with the normal targets at some level.
Similar natural experiments appear to be
exhibited by different breeds of Siamese
cats. These cats all have abnormal routing
of ipsilateral projections to the contralateral lateral geniculate with the result that
the visual field maps are misaligned. When
the lateral geniculate projections reach the
cortex they are dealt with in one of two
ways depending on the breed: they are
either inactivated so as not to interfere with
the remainder of the map or form an isolated independent map that is inserted
adjacent to the otherwise normal map (Kaas
and Guillery, 1973; Guillery, 1974). What
factors bias the axonal competition toward
one or the other option are not known.
Analogous competitive processes may
underly the evolution of new cortical areas.
Simple invasion is astronomically unlikely
because it can only occur when there is a
significant loss of target affinity in one set
RETHINKING MAMMALIAN BRAIN EVOLUTION
of axons and simultaneously a significant
increase in affinity for that same target area
by other axons that have also simultaneously lost affinity for their own target.
Each of these conditions involves an independent mutational event that alters the
respective cell surface molecules or causes
certain whole classes of cells to die. In contrast, invasion by displacement need not
involve any changes in affinity or significant cell death. The only conditions
required are either significant allometric
disproportions between areas or the elimination of some target area or the elimination of some projection as a result of
some degenerative event in evolution.
These conditions are probably not at all
uncommon in the course of evolution. Significant allometric changes in proportions
between different structures and projection systems is the rule in all mammalian
and nonmammalian lineages where brain
size has changed by many orders of magnitude. Such a principle may account for
the parcellation trends in neocortical areas
seen in larger mammalian brains.
Som
DISPLACEMENT THEORIES OF CORTICAL
EVOLUTION: FOUR EXAMPLES
Multiplication of cortical areas and their
differential allometry
The enlargement of the entire cortical
mantle with increasing brain size may influence cortical differentiation indirectly by
altering competitive interactions among
cortical afferents. There may be limits to
the size of a single projection field determined by the number of specific afferents
that are available or by intrinsic functional
constraints. If changes in the size of the
cortical mantle and different thalamic
nuclei are not isometric in the course of
evolution then there may be correlated
changes in the relative size of corresponding projection fields. Changes in proportion may also be influenced by network
allometry influences that impose functional costs on enlarging areas. Such constraints might contribute to the break-up
and duplication of cortical fields in brains
of increasing size. The multiplication of
areas and the differential expansion of some
Rerouting of connections
after fetal lesions of visual
targets maintains visual
function in aberrant targets.
FIG. 15. Misrouting of axons by target elimination
is demonstrated by experiments in which the normal
targets of one projection are eliminated by lesion in
fetal development and the projections to a different
(serially homologous) target are prevented from
forming. In the case depicted here from Frost and
Metin (1985) the targets of the optic nerve—the lateral geniculate nucleus of the thalamus [LG] and the
superior colliculus [SC]—and the target of the lateral
geniculate nucleus—the visual cortex [Vis]—were
damaged by fetal lesions as were the ascending somatic
sensory afferents of the medial lemniscus which would
normally synapse in the ventrobasal nucleus of the
thalamus [VB]. Consequently, the optic fibers were
thereby induced to invade the ventrobasal nucleus of
the thalamus. The otherwise normal projection of this
nucleus to the location of somatic cortex induced this
area to behave as though it were visual cortex.
684
TERRENCE W. DEACON
cortical areas with respect to others may visual association projections (Rakic, 1988).
also be influenced by the total size of the The relative negative allometry of the proentire cortical mantle with respect to the jection nucleus of vision (the lateral genicsizes of other brain structures that are con- ulate nucleus) with respect to the volume
nected with it. This indirect influence is of the corresponding visual associational
suggested by the predictable allometric nuclei (the pulvinar complex) as well as with
scaling of the sizes of cortical areas with respect to the rest of the thalamus (Armthe total size of the isocortex across many strong, 1979; Hopf, 1965; Stephan el al.,
species (Passingham, 1975; Deacon, 1988*). 1981) lends further support to a displaceProbably the most significant determin- ment explanation for this evolutionary
ing factor in such cases is the relative size trend. Figure 16 diagrams the major feaof the afferent projection as compared to tures of this hypothesis in comparison to
its cortical target zone. A cortical target deafferentation experiments. Deafferenarea that has expanded with respect to its tation as a result of adaptational loss or
afferent projections is in some ways anal- reduction of a peripheral sense organ—as
ogous to a cortical area with a reduced in blind cave dwelling species, or to a lesser
afferent projection. Either should produce extent in fossorial or nocturnally speciala decreased density of adjacent correlated ized species—should also produce this sort
inputs which may impair the ability of spe- of effect in cortical areal architecture, but
cific inputs to successfully out-compete and in a brain that is unusually small for this
eliminate diffuse inputs. In the case of pattern in that modality. Studies of such
depleted afferents (Rakic, 1988) the size of naturally deafferented species has demthe differentiated area is reduced and space onstrated reduction of cortical represenis given up to neighboring areas. Neigh- tation of these sensory areas but the issue
boring cortical areas would also face the of projection area to association area ratio
same difficulty. Parcellation of afferent has not been investigated.
projections to form duplicate adjacent proIt is possible that the process of area subjections may thus be a result of reaching division may be a gradual evolutionary
some threshold of competitive instability event in areas with relatively diffuse topodetermined in part by the size of the affer- graphic organization. Area boundaries may
ent map (which may itself be matched in not be discrete and connectional differsize to its peripheral representation by cell ences may exhibit a gradient-like organideath) and in part by the independently zation in these cases. Such incipient area
growing information processing costs of divisions should be more likely in associanetwork allometry within the cortex.
tion areas lacking clear sensory or motor
The increasing proportion of association topographic organization and we should
cortex to projection cortex that correlates expect to see increased individual variation
with increasing brain size could reflect pro- in these areas if this is the case. This patgressive competitive disadvantages for tern should contrast with that of cortical
direct peripheral projection systems in areas that map topographically organized
some modalities, both in recruiting tha- representations of some peripheral modallamic targets and in recruiting cortical tar- ity. In these cases area differentiation
gets via these thalamic projections. This should tend to be more discrete and prewould follow if the proportion of periph- dictable. The border between visual area
eral axons to central axons competing for 17 and 18 and between 18 and 19 is easily
targets declined with size. This kind of tar- distinguishable and correlates with funcget expansion would have parcellation tional map boundaries but the multiple
effects analogous to partial deafferenta- retinotopic maps within area 19 of the
tion. Visual deafferentation experiments monkey cortex are not easily correlated
in monkeys have demonstrated both a with any architectonic borders. The apparreduction in striate cortex area and an ent tendency for middle level cortical assoexpansion into this territory by adjacent ciation areas to exhibit the greatest level
RETHINKING MAMMALIAN BRAIN EVOLUTION
FIG. 16. Displacement theory of association area
expansion is depicted for visual areas in two hypothetical brains of different sizes but receiving input
from eyes that differ little in size. The geniculo-striate
pathway is depicted by solid black arrows and dark
gray targets and the tecto-pulvinar-extrastriate pathway is depicted by dashed gray arrows and light gray
targets (assuming homology of the pulvinar and lateral posterior nucleus). In the expanded brain of B
there has been an expansion of the thalamus and the
cortical target field potentially available for both projections but because the direct retino-thalamic projection is not significantly larger it is not capable of
recruiting a correspondingly larger LGN from the
expanded thalamus and may also be at a competitive
disadvantage in competition for space within the superior colliculus as well with respect to other possible
competing inputs. However, the size of the afferents
to the pulvinar are appropriately enlarged and recruit
a large portion of the thalamus. The consequence for
cortical parcellation is that the geniculo-cortical afferents are at a disadvantage in the competition for cortical territory with respect to pulvinar afferents and
so the striate cortex will occupy a reduced proportion
of the entire visual projection field. The additional
685
of subdivision and functional diversity from
species to species may be a correlate of the
relative fluidity of these divisions.
If the tendency for cortical circuits to
subdivide and differentiate their cortical
targets with respect to one another in
development is exaggerated by brain size
increase and brain size increase is correlated with the evolution of increased body
size, then neither selection for augmented
specialized functions nor selection for generalized brain size increase (and increased
general intelligence) needs to be involved
in order to explain cortical enlargement
and complexincation. Multiplication of
cortical areas might be accounted for, not
as augmentation of function, but as a
response to a growing size differential
between peripheral projections and centrally originating projections as well as a
response to deterioration of integration and
processing efficiency caused by the concomitant reduction of connectivity in a
larger cortex. Advancement of function is
not necessary to explain the multiplication
and differentiation of cortical areas. Functional adaptation is not precluded, but to
demonstrate it requires more than demonstrating an increase in cortical areas and
differentiation of functions within those
areas. These other correlates of size must
be "subtracted" before a proper assessment of functional advancement can be
made. Nonetheless, with the addition of
duplicate areas or with the differentiation
of functions into independent component
processes, new possibilities for specialization arise that could not coexist in a common area. This must certainly be a rich
source for "preadaptations."
Once adaptive alternative connection
patterns are established by whatever means
there may be selection for changes in axon
affinities and other biasing factors that limit
expansion of the pulvinary projection field may further induce its parcellation because of increased
regional differentiation of correlated activity, but also
possibly because the information arriving in the pulvinar may include inputs that reflect the effects of
partially displaced retino-tectal projections.
686
TERRENCE W. DEACON
variability. Because of these developmental biases, parcellation patterns will
become increasingly resistant to reversions, even if the conditions that originally
induced parcellation are undermined. This
suggests that size-induced parcellation patterns may persist even if brain size decreases
in subsequent lineage. Retention of cortical features consistent with a much larger
brain has been documented in a number
of dwarf species (e.g., Warren and Carlson,
1986). This suggests that there may be
functional costs associated with brain size
reduction in evolution. The possibility for
irreversible changes and corresponding
asymmetrical selection against size reduction brings us full circle to a possible progressive or directional tendency in brain
evolution.
Laminar segregation of afferents:
Implications for areal parcellation
and the origins of cortex
Connectional patterns between cortical
areas appear to parallel the cytoarchitectonic differentiation of cortical areas.
Because of this regularity they may provide
some insights into the connectional displacements, invasions and parcellations that
constitute area differentiation in evolution. Tracer studies of corticocortical connections in monkey brains have revealed
characteristic laminar origin and termination patterns that seem to be generalizable to many if not all regions of cortex
(Barbus, 1986; Deacon, 1985; Galaburda
and Pandya, 1983; Jones et al., 1978;
Maunsell and Van Essen, 1983; Primrose
and Strick, 1985; Rockland and Pandya,
1979; Tigges et al., 1973, 1977). In general, connections that originate from association areas and project to areas more specialized for a peripheral sensory or motor
function tend to originate largely from cells
in layer V of cortex and terminate in layers
I and VI. Connections that originate from
specialized (e.g., primary) sensory and
motor areas and project to association areas
tend to originate largely from cells in layer
III of cortex and terminate predominantly
in layers III and IV (see Fig. 17). There
are also subtle gradation differences that
also seem to respect the general "level" of
cortical area. Both origin and termination
patterns are more diffuse across lamina in
association areas (Barbus, 1986; Deacon,
1989a). Similar laminar connection patterns have also been identified in some areas
of cat (Bullier et al., 1984), tree shrew
(Semsa et al., 1984) and rat cortex (Deacon
et al., 1989), but there is too little information for non-primate species to be sure
of its generality.
The consistent association of termination patterns with the architectonic and
functional gradient between association
areas and sensorimotor areas clearly indicates that this hierarchy, which has been
the central feature in all additive theories
of cortical evolution, must also be
accounted for in terms of parcellation and
displacement processes in evolution. Elsewhere (Deacon, 1989a) I have referred to
these reciprocally directed pathways as
centrifugal (limbic-association-sensory/
motor cortex) and centripetal (sensory/
motor-association-limbic cortex) projections because they are oriented with respect
to areas specialized for peripheral information at the one extreme and areas concerned more with internal states of arousal
at the other (see Fig. 18). This hierarchic
chain of cortical areas within each functional modality increases in number of areas
and corresponding synaptic links as brains
enlarge in evolution, yet replicates the same
systematic pattern of laminar connectivity
with each addition. A number of researchers have linked this asymmetric reciprocal
connectivity pattern to Sanides' evolutionary sequence of cortical differentiation (e.g.,
Barbus and Pandya, 1982, 1987; Galaburda and Pandya, 1983; Pandya and Yeterian, 1985). This asymmetry is presumed
to be explainable as a terminal addition
process whereby new areas are always connected to their immediately adjacent
ancestral area by one sort of laminar connection pattern and are reciprocated by its
complement. Despite the rejection of Sanides' theory on a number of grounds, the
correlations it suggests must be accounted
for. With the repudiation of theories claiming terminal addition or terminal differentiation of cortical areas, we are forced
RETHINKING MAMMALIAN BRAIN EVOLUTION
687
parcellation of cortical laminar connectivity
in the process of areal parcellation of isocortex
corticocortical laminar connectivity before parcellation
paralimbic/assn.
•
•
•
•
A
koniocortical
•
corticocortical laminar connectivity after parcellation
FIG. 17. Laminar segregation of corticocortical connections due to functional parcellation of cortical areas
is depicted on the assumption that both the ancestral and developmentally prior state are an undifferentiated
laminar termination pattern. The hypothesized undifferentiated state is depicted in the upper figure as a
single cortical area with intrinsic connections. The subsequent loss of selected classes of connections with area
parcellation is depicted in the lower figure. Note that the culled connections are asymmetric with respect to
their directional orientation. Possible sources of competitive bias that might drive this asymmetric parcellation
during development are discussed in the text.
to explain this correlation between architectonic gradients, asymmetrically patterned reciprocal connections, inverse
maturational gradients, and the apparent
functional hierarchy of cortical areas in
terms of competitive biases and displacement processes in cortical development.
At the present time there is no developmental information concerning corticocortical laminar differentiation processes. Nonetheless, speculation concerning
the possible mechanisms involved can be
concentrated on a few plausible factors. For
corticocortical connections within a cortical area there does not seem to be this level
of laminar specification (Rockland and
Pandya, 1979). The differentiation of a new
cortical subdivision out of a single ancestral
area must therefore correlate with a loss
of projections to certain cortical lamina.
Furthermore the loss is different depending upon whether the projection is in the
centrifugal or centripetal direction. Since
the appearance of a new cortical division
must be a consequence of the competitive
parcellation-displacement processes, these
systematic connectional losses likely correlate with competitive asymmetries
between different afferent populations.
This suggests that we should look for corresponding biases, either in terms of heterochronic, allometric or functional differences, that distinguish association areas
from specialized sensory/motor areas. In
fact, all three possible sources of bias can
be identified and are probably not independent.
Another important clue concerning the
particular cortical lamina that distinguish
these different cortico-cortical projections
comes from the finding that different
classes of thalamo-cortical projections also
appear to segregate according to terminations in these same lamina. Multiple thalamic nuclei project to each cortical area
but tend to terminate in different lamina
within the same area. It appears that principal thalamic projection nuclei, whose
projections are generally limited to a single
architectonic area, tend to terminate in
columnar fashion within layers III and IV,
usually coinciding with the distribution of
granule cells in those layers (Diamond,
1982; Frost and Caviness, 1980). Intralaminar thalamic nuclei, which exhibit relatively non-specific projections to many cortical areas, tend to terminate in layer VI
(Frost and Caviness, 1980; Herkenham,
688
TERRENCE W. DEACON
centripetal
centrifugal
FIG. 18. Centrifugal and centripetal corticocortical
projections are depicted on idealized flattened maps
of the cerebral cortex of one hemisphere. Areas
depicted in darkest gray are koniocortical areas or
specialized motor cortex, those in lightest gray are
association cortex, and the white perimeter represents limbic cortex. The arrows represent multisynaptic pathways from area to area extending either
from koniocortex (or motor cortex) to intermediate
association cortex to paralimbic association cortex to
limbic cortex (centripetal) or from limbic cortex to
paralimbic association cortex to intermediate association cortex to koniocortex (or motor cortex). The
terms centripetal and centrifugal are chosen not
because of their spatial connotations (which may be
somewhat confusing in this depiction) but because of
the orientation of these projections with respect to
the gradient between areas specialized for peripheral
systems and those representing regulation of internal
1980; Rausell and Avendano, 1985). Other
nonspecific nuclei that project to many
areas within the same modality and midline
"limbic" nuclei, which also exhibit widespread paralimbic cortex and association
cortex projections, tend to terminate in
layer I (Diamond, 1982; Friedman et al.,
1987; Frost and Caviness, 1980; Rausell
and Avendano, 1985). The non-specific and
limbic nature of thalamic projections to
layers I and VI and the specific projections
to layers III and IV can be interpreted as
functionally analogous to their counterparts among cortico-cortical projections in
a number of ways. Middle layer projections
appear always to introduce information
associated with a source that is more
directly connected with the peripheral nervous system than are their target, whereas
deep and superficial layer connections
appear to convey information that ultimately derives from systems involved more
with the regulation of internal state, as well
as attentional and emotional arousal (Deacon, 1989).
Two important heterochronic maturational factors differentiate cells and axons
in these cortical lamina. These correlated
heterochronic differences may account for
which lamina tend to be associated with
which afferents by virtue of their biasing
influence on axonal competition. Neurons
occupying positions that would be superficial to layer II and deep to layer VI in the
adult brain mature before the cells of the
cortical plate and form a single primordial
cortical layer. Cells in the outer layer called
Cajal-Retzius cells exhibit two large
"extraverted" dendrites that extend up
toward the pial surface and laterally,
whereas cells in the deep layer called Martinotti cells are of a distinct bipolar shape
with dendrites extending more superficially and deeper. Cortical plate cells
migrate into position in an inside-out pattern between these two cell layers. The earliest cortical plate cells to mature are the
deep layer V and VI pyramidal cells and
the very last cortical plate cells to mature
are the most superficial pyramidal cells of
layers II and III and the granule cells of
layer IV. Prior to the appearance of the
cortical plate neurons both primordial cell
types appear to establish transient synapses
RETHINKING MAMMALIAN BRAIN EVOLUTION
with early afferent projections (Marin-Padilla, 1978). Although there is some disagreement on the ultimate fate of these
early maturing cells most argue that they
are eliminated by programmed cell death
in most isocortical areas (although they
appear to persist in entorhinal cortex and
in paralimbic isocortex in small species, and
in all cortical areas in cetaceans; see next
section).
Although it is not known what structures
give rise to the afferent projections that
synapse on the transient cells above and
below the developing cortical plate, it is
reasonable to assume that they arise from
the same structures that will later innervate the corresponding deep and superficial lamina in the adult. The fact that the
thalamic afferents terminating in layers I
and VI are nonspecific projections and do
not appear to respect cortical boundaries
(Cavinessand Frost, 1980; Diamond, 1982)
may reflect their arrival prior to cortical
plate afferents that subdivide the cortex
into discrete functional regions. If the
transient cells to which these early axons
contact are later eliminated, these axons
may be displaced onto adjacent pyramidal
cell dendrites in the deepest and most
superficial lamina of cortex (see Fig. 19).
Since different target cells within the cortex mature at different times and different
projections arrive at different times, temporal correlation may play an important
role in biasing laminar connection patterns
from both thalamic and cortical sites. Early
maturing cells located deep and superficial
to the cortical plate may correlate with the
early maturing projections and late maturing small cortical plate cells in layers III
and IV may correlate with relatively late
maturing projections. Differences in the
relative maturation times of cells and axons
from one cortical area to another might
additionally contribute to areal differences
in laminar organization.
Another possible heterochronic bias that
may influence the asymmetric directionality of these projections can be discerned
in the differential myelination of thalamocortical fibers from principal thalamic
nuclei projecting to these areas. Since myelination appears to precede from specialized areas to association areas the compe-
689
tition for synapses in the middle cortical
lamina may be biased by earlier myelination of areas that are more peripherally
specialized.
An allometric bias is reflected in the
expansion of association areas relative to
sensory/motor specialized areas in larger
brains. As noted above, this suggests that
primary projection areas, which are more
directly linked to peripheral systems, are
competitively constrained by the size of
their afferent projections, whereas afferents to association areas have no such
extrinsic constraints. This may also be the
reason these areas appear to exhibit less
clearcut cytoarchitectonic divisions. The
gradient in architectonic specialization is
one of the primary bases for Sanides' argument.
Functional differences are also consistently correlated with this gradient. For
example, neurons in striate cortex appear
to be precisely "tuned" to specific, highly
localized stimulus attributes and are organized according to precise retinotopy
whereas neurons in inferotemporal visual
association areas, at the extreme opposite
end of the hierarchy, seem tuned to global
stimulus attributes and exhibit very large
receptive fields with no obvious topographic organization. This is undoubtedly
also a correlate of the relative directness
or indirectness of their respective retinocortical afferent circuits (Kaas, 1989). At
the sensory end of the spectrum of areas
input from the periphery is highly variable
and functional correlation is only exhibited over very short distances, whereas at
the association end input is primarily limbic and likely highly intercorrelated over
relatively larger distances. Since functional
specialization of cortical areas can be significantly affected by sensory experience
during early development it is almost certain that differences in the correlation of
afferent signals among adjacent axons play
a major role in determining which connections persist into adulthood.
Finally, the issue of network allometry
should be considered. Given the fact that
break-up of previously integrated functional areas effectively distributes processing across a number of areas, functioning
to some extent in parallel, cortico-cortical
690
TERRENCE W. DEACON
Pattern of isocortical neurogenesis
and probable programmed cell death
x
prior to the
cortical plate
early invasion by
cortical plate cells
addition of cortical
plate cells to
external layers
VI
probable elimination of cells
outside the cortical plate and
specialization of granular layer
Hypothesis to account for development of
laminar specificity of cortical afferents
VI
early non-specific
thalamic (+?) projections
displacement of non-specific
projections from eliminated
cells to cortical plate cells and
invasion of specific thalamic projections
FIG. 19. A theory of the displacement processes involved in laminar parcellation of cortical afferent connections and a possible explanation of their relationship to neocortical origins. The upper figure diagrams
the events of corticogenesis assuming programmed cell death of cells that precede the formation of the cortical
plate (it is also possible that some of these precursor neurons are converted into neurons with different
morphologies in the mature cortex). Note that neurons forming the cortical plate migrate into position
between the two groups of cortical precursor cells above and below it and deposit in the uppermost layers of
the developing cortical plate. The lower figure depicts a displacement theory for the laminar segregation of
cortical afferents during cortical development. It is hypothesized that axons from early maturing non-specific
thalamic nuclei and possibly early maturing limbic areas establish synapses on the two populations of cortical
precursor cells and maintain them as cortical plate neurons are added and the two cell groups are forced
apart. After the formation of the cortical plate selective cell death of the precursor cells forces the axons
attached to them to seek alternative synaptic contacts. They are displaced onto the dendrites of cortical plate
neurons of the same lamina. These axons may have a competitive advantage because of their numbers and
functional maturity compared to the later arriving principal thalamic afferents that target cortical plate cells
in middle layers. The two independent populations of neurons, superimposed by the migration of the cortical
plate cells, with different maturation schedules and distinct classes of afferents, suggest the possibility that
they derive from independent phylogenetic origins—the precurser cells from the dorsal cortex and the cortical
plate cells from the dorsal ventricular ridge of a reptilian ancestor.
RETHINKING MAMMALIAN BRAIN EVOLUTION
connections should be competitively
selected during development that maximize intercorrelated function and most
efficiently distribute processing demands
throughout the network. Such an interpretation is suggested by the "counter-current" organization of these cortico-cortical
connections (see Deacon, 1989).
This analysis of laminar maturation and
connectional differentiation suggests an
alternative interpretation for the origins of
mammalian isocortex that combines both
the equivalent cell hypothesis and invasion
by displacement. My suspicion (also suggested in Marin-Padilla, 1978) is that these
transient cells are the homologues of the
cells of the ancestral dorsal cortex of reptiles and that the cortical plate cells represent a phylogenetically later intrusion
(following the equivalent cell hypothesis)
perhaps from ancestral dorsal ventricular
ridge positions. The death of the reptilian
dorsal cortex homologue cells in mammalian ontogeny induces the displacement
of their afferents onto the apical dendrites
of these recently juxtaposed nonhomologous cells of the cortical plate. The cortical
plate cells also maintain their original
afferents and thereby establish a novel
integration of these ancestrally separated
and independent circuits. In this sense the
laminar termination pattern of centrifugal
pathways links them with the ancestral dorsal cortex system (which has always been
linked with limbic cortex) and the laminar
pattern of centripetal pathways links them
with the ancestral dorsal striatal (dorsal
ventricular ridge) system.
Cetacean brain evolution
In the preceding sections I have referred
to a number of general trends in mammalian brain evolution that appear to be
strongly correlated with brain size. These
include both microscopic and macroscopic
features of brains, all of which ultimately
have to be understood in terms of biases
and constraints that modify developmental
processes, and most particularly, axonal
competition/parcellation processes. It is
therefore important to consider exceptional cases where these correlations do not
seem to hold. Understanding what it is
691
about these brains that causes them to
diverge from these otherwise ubiquitous
trends will unquestionably provide important insights into the causes for the general
trends.
Some of the most striking exceptions can
be found in the dolphin brain (and presumably in all cetacean brains). Many of
the unique architectonic features of the
dolphin brain have been meticulously documented in a series of recent papers (Jacobs
et al., 1971, 1979, 1984; Morgane and
Jacobs, 1972; Morgane et al., 1982, 1985,
1986a, b, 1990). These findings concerning the dolphin brain are paradoxical in
the context of traditional theories of mammalian brain evolution because they suggest that dolphin brains combine features
that are considerably highly advanced with
features that are considered quite primitive and conservative (Glezer et al., 1988).
The highly advanced features of the dolphin brain are largely macroscopic morphological features, including a large brain
size, a high degree of encephalization, a
highly convoluted cortex, a high ratio of
neocortex to total cortex (and therefore a
high ratio of neocortex to limbic cortex),
and apparently (although this is difficult to
assess accurately) a large percentage of
association cortex. Dolphins also are considered to be among the most behaviorally
advanced species by many behavioral
researchers (Herman, 1980; Wiirsig, 1989;
but see critique by Gaskin, 1982). In contrast, their conservative traits are largely
microscopic features, including relatively
thin and poorly laminated isocortex, essentially agranular (and some would argue,
nonexistent) layer IV and therefore no typically definable koniocortical areas, apparent lack of a gigantopyramidal cortex {i.e.,
architectonically specialized primary motor
cortex; although some evidence of this can
be discerned in the form of larger layer V
pyramidal cells), remarkably thick layer I
with respect to the rest of the cortical layers, a well developed layer VI, densely
packed layer II, large "extroverted" cells
in layer II, poorly defined columnar organization, indistinct architectonic boundaries, and apparent adjacency of sensorymotor projection areas with respect to
692
TERRENCE W. DEACON
dorsal view
lateral view
FIG. 20. Drawing of the dolphin brain shown in simplified form from above and from the side with an
indication of the topographic position of the different
sensory and motor projection fields. Although the
areas depicted are presumed to be the primary projection areas for these modalities, I prefer to reserve
judgement on this hornological relationship. Assuming that the indicated areas are representative of the
proportion of neocortex occupied by primary areas
it would appear that most of the dolphin isocortex is
composed of association areas. Limbic cortex is not
visible from the lateral view and is a relatively small
proportion of the total cerebral cortex as is appropriate for a mammalian brain of this size. Information
for this drawing is derived from Morgane et al. (1986a,
by.
association areas (see Fig. 20). Glezer et al.
(1988) are compelled to designate a special
"conservative-progressive mode" of cortical evolution to account for this anomalous combination of features.
The dichotomy between macroscopic
morphological features and microscopic
cytoarchitectonic features is undoubtedly
significant for understanding this apparent
paradox. If gross morphological traits were
the only available evidence then the dolphin brain would be ranked along with the
human brain as a highly advanced brain.
Such traits as the ratio of neocortex to limbic cortex follow expected allometric predictions of a brain the size of a dolphin
brain. It is in fact even more convoluted
than might be expected from a terrestrial
brain of such size, but this can probably be
explained on the basis of its relatively thinner cortex (compared to terrestrial mammal brains of similar proportions—e.g.,
primate and human brains). Thus, with
respect to the production of initial cell populations the dolphin brain probably shares
common mechanisms with all mammals.
But the production of cell populations and
the parcellation of those populations into
distinct functional divisions and architectonic fields occur independently at separate developmental stages. The cortical
architectonic parcellation process occurs
subsequent to the production of the cortical mantle. The allometric proportions
that will determine the proportion of isocortex to limbic cortex and projection cortex to association cortex are already established, but the competitive interactions
which will subdivide and specialize these
cortical targets have not begun at this stage.
We must look to this latter process for a
clue to the peculiarities of the dolphin
brain.
The hypothesis I will suggest to explain
these architectonic peculiarities focuses on
the agranularity of dolphin cortex. The
lack of layer IV granule cells throughout
the cortex of the dolphin brain is particularly remarkable because the origin of
granule cells is far more ancient than the
divergence of Cetacea from the rest of the
eutherian stock. Despite the fact that small
brains are in general less "granularized"
RETHINKING MAMMALIAN BRAIN EVOLUTION
(a feature that may in part be attributed to
the fact that the size difference between
granule cells and pyramidal cells in small
brains is relatively slight), even the apparently primitive brain of the North American opossum Didelphis virginiana exhibits
a clearly denned and even subdifferentiated layer IV that receives dense principal thalamic afferents (Johnson, 1988;
Walsh and Ebner, 1970), so the common
eutherian ancestor of cetaceans and other
eutherian mammals doubtless also possessed layer IV granule cells. Complete loss
of this cell type and cell layer must be considered a rare derived trait.
The absence of granule cells is of primary significance with respect to the process of architectonic parcellation of cortical areas. During development of cerebral
cortex, it is the competition between invading axons from the major thalamic nuclei
that is largely responsible for the specification of topographic maps and establishment of functional and architectonic
boundaries. As is well demonstrated by
studies of the formation of somatosensory
barrels in the rat and ocular dominance
columns in the cat and monkey, the terminations of these critical projections and
the principal layers in which this competition takes place are layers III and IV,
corresponding to the distribution of granule cells in those layers (Jacobson, 1978).
Experimental destruction of these thalamic afferents at an early stage (Rakic,
1988) or even elimination of peripheral
sensory information to these afferents
(Woolsey and Wann, 1976) is capable of
profoundly altering the structure of the
resultant cortical map and even causing
functional-architectonic boundaries to be
displaced (Rakic, 1988).
Given the developmental importance of
this thalamofugal-granule cell relationship
for architectonic differentiation, it is clear
that elimination of this cell type and displacement of many or all of the thalamofugal projections to other layers and other
cellular targets in the developing cerebral
cortex of the dolphin brain is bound to
profoundly alter all features of its tangential and radial organization. The relatively
thick layer I likely results from the dis-
693
placement of thalamic afferents that lack
their "normal" primary affinity targets.
The presence of unusual "extroverted"
cells in layer II whose large dendrites reach
up into layer I may also be explainable in
this way. These cells are present in fetal
mammals brains but are eliminated early
in development and are the targets for
transient synapses during the early stages
of cortical differentiation (see discussion in
the previous section). The displacement of
axons lacking their principal target into
this layer at an early stage of development,
prior to the "normal" elimination of these
cells, may allow them to persist by establishing permanent synapses with the
orphaned principal thalamic afferents.
Since columnar organization is established
by competitive exclusion processes within
layer IV and deep layer III, the lack of
clearly delineated columnar organization
undoubtedly results from the absence of
axonal competition in this layer. The lack
of clear architectonic boundaries and the
apparently clustered projection areas all
reflect this significant reduction of axonal
competition processes. However, the presence of differences in relative cell sizes in
different layers and differences in the density of pyramidal cells in different areas
(e.g., between somatosensory and motor
areas, Morgane et al., 1986a), that correspond to similar differences in terrestrial
mammals, indicate that these features are
probably controlled by factors other than
thalamocortical connections, most probably their efferent terminations. These
hypothetical effects of granule cell elimination are depicted in Figure 21. Neurological mutant mice that completely lack
cerebral cortical granule cells should also
exhibit many of these same characteristics.
Study of these could serve as an informative test case, although because of the great
size difference many of the most unusual
features of the dolphin brain might not
express themselves so obviously in a mouse.
How and why cetacean brains lost their
granule cells is a mystery. It is a trait that
is probably shared by all cetaceans and so
was inherited from their common ancestor
subsequent to their divergence from terrestrial mammals. There is some trace of
694
TERRENCE W. DEACON
Monkey
thalamic afferents
Dolphin
thalamic afferents
FIG. 21. Granule cell degeneration hypothesis of the dolphin isocortex is depicted in comparison with the
pattern typical for terrestrial mammals represented by the monkey brain. In the diagram of monkey cortical
architecture the droplet shaped cells represent pyramidal cells the small spherical cells in middle layers
represent granule cells and other small interneurons and the ellipsoid cells represent small layer II cells. The
same shapes are used to depict cells in the diagram of the dolphin cortex with the exceptions that there are
no granule cells and some of the layer two cells are assumed to be embryologically retained "extraverted"
cells. Principal thalamic afferents that normally would target the granule cell populations in the cortical plate
are induced to establish alternative targets in the dolphin cortex in which granule cells are strangely absent.
These afferents without normal targets may establish synapses with embryonic cells in layer II that would
otherwise be eliminated after cortical plate formation in terrestrial mammals. These retained layer II extraverted cells and the displaced thalamocortical axons cause layer I to be disproportionately thick and layer II to
be more densely packed than is observed in terrestrial mammal brains. Middle layers are also prevented from
normal competitive parcellation into columnar units, that otherwise would distinguish specialized koniocortical
areas. This suggests that dolphin cortical organization is neither conservative nor atavistic, but highly derived.
this degenerative process left because
immature dolphin brains do exhibit a transient but thin layer IV that disappears by
maturity (Garey and Leuba, 1986). It seems
unlikely that this loss can be rationalized
as an adaptation. Given the total breakdown of cortical differentiation processes
that resulted one would presume that this
is a costly mutation, although even in the
mutant Reeler mouse, with its totally disrupted cortical architecture (but not lacking granule cells), the cortex still functions
and allows for adequate perceptual and
motor functions. The fixation and survival
of this trait in cetaceans as opposed to any
other terrestrial lineage may be related to
their unusual and relatively complete adaptation to the aquatic habitat. The lack of
"granularized" competitors (e.g., pinnipeds) in this niche until much later in
mammalian evolution may have been cru-
cial to the persistence of this trait. Consider
the significance of the regression of many
specific sensory and motor systems in these
species associated with their aquatic adaptation. They are anosmic, they have significantly reduced visual requirements (and
in this regard are comparable to fossorial
species and echolocating bats with secondarily reduced visual systems), and they
exhibit significant reduction of the distal
limbs, shoulder girdle and pelvis (which in
terrestrial vertebrates comprise the predominant afferent and efferent representation of the primary somatosensory and
motor fields). Although many species have
highly developed echolocation systems, this
appears far more substantially represented
by collicular specialization (evidenced by
the immensely expanded and highly differentiated inferior colliculus) than by cortical specialization. All these regressive fea-
RETHINKING MAMMALIAN BRAIN EVOLUTION
tures appear to coincidentally correlate
with the inability of the dolphin cortex to
architectonically differentiate.
In summary, this exception appears to
prove the rule in a rather striking and
unambiguous way. The problems of determining whether the dolphin brain is conservative or advanced or conservativeadvanced are irrelevant. The dolphin brain
is none of these. It is highly derived. These
problems that arose in the analysis at the
level of comparative morphology and comparative cytoarchitecture dissolve once we
approach the question from the perspective of developmental homologies.
Human brain evolution
Assumptions about human brain evolution are the ultimate source for many of
the misleading ideas that have haunted the
study of brain evolution, so it is fitting that
the exorcism of these ideas in this paper
should conclude with a discussion of the
uniqueness of human brain evolution. Two
unique characteristics of the human brain
stand out as central. The human brain is
roughly three times larger than would be
predicted for an anthropoid primate of
human body size, and human brains are
capable of acquiring an unprecedentedly
complex and flexible communication system—language. These two facts are
undoubtedly linked.
Beginning with the issue of human brain
size, it is important to find out if this
increased cell production follows trends
that are typical in other members of the
primate order. This can be ascertained by
comparing the relative sizes of the various
major structural divisions of the human
brain with predictions based on trends for
primates in general. Initial evidence that
there is a deviation from predicted allometries comes from an examination of
studies that have used brain structure volumes to construct possible phylogenetic
trees for primate ancestry. Two studies,
using largely similar data but different
methods that control for the effects of brain
size (Douglas and Marcellus, 1975;
Bauchot, 1982), have concluded that the
human brain is more similar to either one
of two New World monkeys' brains (woolly
695
monkeys in one case and capuchin monkeys in the other) and one Old World monkey's brain (Cercopithecus talapoin), than to
the brains of any other Old World monkeys and apes. It is probably not coincidental that those primates most closely
linked with Homo by these studies also represent relatively encephalized primates.
When a structure by structure allometric
analysis is performed it appears that the
human brain diverges from primate trends
in a number of striking ways. Based on predictions from primate trends, the cerebral
cortex and cerebellar cortex of the human
brain are disproportionately large relative
to the diencephalon, corpus striatum, brain
stem and spinal cord (Deacon, 1984,
1988ft). This is depicted in Figure 22.
The production of neurons that constitute cortical structures takes place well
before any axonal parcellation processes
begin, and therefore, as noted earlier, the
increase in cerebral cortex cannot be specific to any particular region of cortex. The
increase in radial dimensions of the cortical
germinal field and in the number of ontogenetic columns that will differentiate out
of it must take place in the human brain
prior to neural production within the cortex. The size disproportions between the
expanded neocortical target field, the relatively unexpanded population of thalamofugal axons, and the relatively unexpanded efferent subcortical targets of
cortical neurons must significantly bias
parcellation processes in all these areas
during subsequent stages of differentiation. One effect of this is apparent in deviations of relative cortical area dimensions
with respect to predictions based on the
allometry of these structures in other primates. Some cortical areas appear significantly smaller than expected for a primate
brain this size and others significantly
larger. For example, the visual cortex
appears to scale appropriate to the size of
its peripheral input (the retina) and its
principal thalamic nucleus, but does not
occupy the proportion of cortex predicted
for a primate brain of this size. Its peripheral sources are constrained by the small
human body size with respect to the large
brain size. As a result they do not scale to
696
thaiamocortical parceilation
process in a brain with typical
primate cortical-nuclear proportions
TERRENCE W. DEACON
thaiamocortical parcellation
process in the human brain with
disproportionately enlarged cortex
FIG. 22. Schematic diagram of large-scale human
brain structure disproportions and their effects on
axonal competition processes during human development as compared to development in the absence
of these human disproportions. With respect to the
predictions based on other primate brains, human
cortical structures (including the entire cerebral and
cerebellar cortices) are larger than expected with
respect to brainstem, cerebellar, diencephalic and
telencephalic nuclear structures. Since the cell production processes which determine the gross size of
these major morphogenetic fields are completed prior
to their parcellation into functional subdivisions it is
predicted that these disproportions will result in biased
displacement processes. The typical condition is
depicted by the three brains on the left. Brains A and
B represent the normal developmental stages of cortical axonal parcellation of visual (gray cortex with
gray dashed arrows as afferents), somato-motor (black
with black arrows) and prefrontal (gray with solid gray
arrows) cortical fields in a large primate. The human
deviation from this is depicted by the three brains on
the right. Brains C and D represent the human developmental stages with constraint of visual and somatic
fields by their unexpanded peripheral afferents and
displacement by prefrontal afferents producing a much
enlarged adult prefrontal area.
the level that would otherwise be predicted
on the basis of brain size (a brain this big
would be expected only in a very very large
ape—the "King Kong" null hypothesis of
human brain evolution). The competitive
limits for these afferent systems are constrained by the size of the peripheral input.
Preliminary data suggest that this is probably also the case for auditory, somatic and
FIG. 23. A diagram of some of the relative proportions of cortical fields in the human brain as compared
to predictions based on typical anthropoid primate
trends. The percentages represent absolute deviations from the predictions for a primate brain of human
size. Temporal, parietal, and motor area predictions
are based on too few data points to be significant, but
demonstrate a pattern that is consistent with the findings for other areas and with the displacement
hypothesis. The depiction of peripheral structures
associated with different cortical areas is intended to
indicate that cortical areas with relatively direct representation of peripheral sensory or motor systems
are constrained by these afferents or efferents in their
competition for cortical representation. Figure taken
from Deacon (19906).
motor areas as well as for visual areas (Deacon, 1984, 19886; see Fig. 23).
The competitive limitation of these projection systems translates into a competitive advantage for other areas not constrained by peripheral afferents, which
must inherit the cortical surface area that
is left unrecruited as a result. The prefrontal zone appears to be one major beneficiary of this competitive imbalance. It is
estimated to be approximately twice the
size expected for a primate brain of human
proportion (and this translates to six times
the size predicted for a primate of human
RETHINKING MAMMALIAN BRAIN EVOLUTION
617
FIG. 24. Some predicted connectional consequences of prefrontal enlargement are represented by brain A
(typical primate brain structure allometry) as compared to B (human cortical-nuclear disproportion). Displacement theory suggests that the enlargement of the number of prefrontal efferents competing for midbrain
targets as compared to diencephalic efferents will bias competition in favor of prefrontal projections which
will displace both some diencephalic, limbic and intrinsic midbrain axons from their normal targets. This may
lead to the relative dominance of prefrontal outputs over limbic and diencephalic outputs in control of midbrain
and brainstem vocalization centers and motor circuits. This may be linked to adaptations associated with
language skills and the loss of many stereotypic vocalizations in human evolution.
body size; Deacon, 1984, 19886). Prefrontal cortex is not a recipient of peripheral
inputs, but of inputs from other nonspecific and polymodal systems of the midbrain and cerebral cortex. It is thus buffered by being synaptically removed from
the cascading effects of peripheral bias that
affect other systems. It is probably not incidental that Broca's area for speech is contained within this enormously expanded
field.
This disproportionate prefrontal cortical surface area is a secondary consequence
of the initial disproportion of the entire
embryonic cortex with respect to its subcortical-peripheral connections. These initial disproportions biased axonal competition for cortical representation in favor
of cortical areas whose afferents were not
constrained by peripheral systems. But this
secondary disproportion of prefrontal areas
itself must have other tertiary biasing consequences. Deacon (1990c) notes that efferent projections of this system target limbic
cortical structures and a range of midbrain
structures. We can expect prefrontal projections to have a significant competitive
advantage over other afferents to these
areas during development (see Fig. 24). The
midbrain targets of prefrontal projections
also receive descending limbic cortical and
hypothalamic projections and intrinsic
projections from the central gray and reticular formation. Many of these prefrontal
and limbic cortical targets turn out to play
major roles in vocal call production in primates. The displacement of "normal"
afferents of these areas and replacement
by a larger fraction of prefrontal axons may
have significantly altered their function.
Deacon (1990c) argues that this may
account for the significantly reduced repertoire of stereotypic call types in humans,
as well as for the recruitment of some of
these systems by cortical areas capable of
supporting complex skilled motor programming. The disproportions among
cortical areas and the relative reduction of
thalamocortical as opposed to corticocortical axons undoubtedly also played a role
in altering cortical functions, some of which
are related to the human language capacHuman brain evolution cannot be conceived in the terms of a conservative-progressive scheme of mammalian brain evolution. Our brains are not at the pinnacle
of any evolutionary trend. Rather the
human brain is an unusual divergent case.
The extreme disproportion of human brain
size with respect to the human body size
with respect to other primates and mammals is only a surface manifestation of a
complex allometric reorganization within
698
TERRENCE W. DEACON
the brain, and is unlikely itself to be the
crucial trait under selection in human evolution. It is not just the increase in cortical
complexity nor the increased relative size
of the whole brain but the correlated reorganization of underlying neural circuitry
that is probably most significant to human
uniqueness (see also Holloway, 1979).
Because the data that we possess concerning the human brain are still necessarily limited to morphological information and notably do not include detailed
connectional data (due to the invasive
nature of present tracer techniques) direct
verification of these hypothetical reorganizations will have to wait. However, our
understanding of the processes that must
underly development of a brain with the
allometric characteristics of the human
brain can be further augmented by continuing investigations of the relationships
between allometric and developmental
processes shared by all mammals. The
details of human brain evolution are still
largely obscure. The hypotheses presented
here are based on a massively incomplete
set of data. And yet the basic underlying
logic of allometric change and axonal displacement processes during development
has provided an important new window
through which to view these data and an
indispensable guide to the gathering of
subsequent information about human brain
structures and human development.
Conclusions
Understanding the evolutionary ancestry of the brain's organization is not merely
an academic exercise. It is crucial to the
study of its basic functional processes as
well. Few if any brain structures initially
evolved their present form precisely for
the purposes they now serve, and many
current brain systems may be the result of
lucky syntheses of previously separated circuits or else the result of fortuitous degenerative events. Because the brain was not
predesigned for its current adaptations the
strategies employed in its operation will not
likely yield to a purely functional physiological analysis. More importantly, an
understanding of the predispositions and
constraints inherited from past adaptations
and developmental strategies can lead us
beyond a merely superficial understanding
of function to appreciate some of the
deeper fundamental organizing principles
shared by all features of the brain.
Neither the study of mammalian brain
evolution nor even the study of human
brain evolution is limited to merely theoretical exploration. We currently have
access to experimental tools that are adequate to the task of analyzing the neural
developmental processes that underly, canalize and constrain brain evolution, and are
capable of gathering the sorts of comparative anatomical evidence that can elucidate the variety of ways these processes
have been expressed in evolution. This is
an invaluable complement to other areas
of the neurosciences that are rapidly building a database of comparative physiological and behavioral information. Our failure to immediately grasp the significance
of these data for brain evolution has largely
been the fault of the unrecognized influence of some very old notions about evolution, the nature of mental processes, and
the place of humans in some cognitive scala
naturae.
The displacement hypothesis has led me
to propose four highly speculative explanations of some major problems in mammalian brain evolution. But displacement
theory does not depend on the correctness
of these particular interpretations. In fact,
it provides means to falsify them if they are
incorrect. The theory clearly requires that
patterns of brain evolution be explained in
terms of the biasing of competitive developmental mechanisms and suggests
numerous possible candidates for biasing
influences that are likely involved: including (in order of likely importance) allometric relationships, cell death, heterochronous changes in maturational events
and changes in molecular affinities between
cells and axons. The correlates of displacement processes that are postulated to
account for an evolutionary change must
be physically exhibited by the developmental processes that construct living
brains. If they are not observed then a displacement explanation must be rejected or
modified. The examples presented in this
RETHINKING MAMMALIAN BRAIN EVOLUTION
699
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