Download Identified nerve cells and insect behavior

Progress in Neurobiology 63 (2001) 409 – 439
www.elsevier.com/locate/pneurobio
Identified nerve cells and insect behavior
Christopher M. Comer a,*, R. Meldrum Robertson b
a
Laboratory of Integrati6e Neuroscience, Department of Biological Sciences, Uni6ersity of Illinois at Chicago, Chicago, IL 60607, USA
b
Department of Biology, Queens Uni6ersity, Kingston Ont., Canada K7L 3N6
Abstract
Studies of insect identified neurons over the past 25 years have provided some of the very best data on sensorimotor integration;
tracing information flow from sensory to motor networks. General principles have emerged that have increased the sophistication
with which we now understand both sensory processing and motor control. Two overarching themes have emerged from studies
of identified sensory interneurons. First, within a species, there are profound differences in neuronal organization associated with
both the sex and the social experience of the individual. Second, single neurons exhibit some surprisingly rich examples of
computational sophistication in terms of (a) temporal dynamics (coding superimposed upon circadian and shorter-term rhythms),
and also (b) what Kenneth Roeder called ‘neural parsimony’: that optimal information can be encoded, and complex acts of
sensorimotor coordination can be mediated, by small ensembles of cells. Insect motor systems have proven to be relatively
complex, and so studies of their organization typically have not yielded completely defined circuits as are known from some other
invertebrates. However, several important findings have emerged. Analysis of neuronal oscillators for rhythmic behavior have
delineated a profound influence of sensory feedback on interneuronal circuits: they are not only modulated by feedback, but may
be substantially reconfigured. Additionally, insect motor circuits provide potent examples of neuronal restructuring during an
organism’s lifetime, as well as insights on how circuits have been modified across evolutionary time. Several areas where future
advances seem likely to occur include: molecular genetic analyses, neuroecological syntheses, and neuroinformatics — the use of
digital resources to organize databases with information on identified nerve cells and behavior. © 2001 Elsevier Science Ltd. All
rights reserved.
Contents
1.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
1.1. The identified neuron approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410
2.
Coding sensory information . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Historical perspective . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. Operating principles . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1. Group/individual differences in central circuitry . . . . . . .
2.2.2. The importance of temporal dynamics . . . . . . . . . . . .
2.2.3. The computational sophistication of individual neurons . .
2.2.4. Coarse coding and computational mapping. . . . . . . . . .
2.2.5. Neural parsimony and the decoding of central information
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Abbre6iations: 5HT, 5-hydroxytryptamine (serotonin); AN, ascending neuron; CN cercal nerve; CNS, central nervous system; DCMD,
descending contralateral movement detector; DEP, depressor; DMI, descending mechanosensory interneuron; DN, descending neuron; DUM,
dorsal unpaired median; EPSP, excitatory post synaptic potential; GABA, gamma amino butyric acid; GF, giant fiber; GI, giant interneuron; dGI,
dorsal giant interneuron; vGI, ventral giant interneuron; int, interneuron; STIM, stimulation; TCG, tritocerebral commissural giant; TI, thoracic
interneuron; T3, metathoracic ganglion; URL, universal resource locator; VM, ventromedial; VUM, ventral unpaired median; VCH, ventral
centrifugal horizontal; WBF, wing beat frequency; WWW, world wide web.
* Corresponding author. Tel.: + 1-312-9962992; fax: +1-312-4132435.
E-mail addresses: [email protected] (C.M. Comer), [email protected] (R.M. Robertson).
0301-0082/01/$ - see front matter © 2001 Elsevier Science Ltd. All rights reserved.
PII: S0301-0082(00)00051-4
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
410
3.
Patterning of motor output. . . . . . . . . . . .
3.1. Historical perspective . . . . . . . . . . . .
3.2. Operating principles . . . . . . . . . . . .
3.2.1. Defined circuits . . . . . . . . . . .
3.2.2. Organization and reconfiguration
3.2.3. Neuromodulation. . . . . . . . . .
3.2.4. Afferent regulation . . . . . . . . .
3.3. Neuronal restructuring . . . . . . . . . . .
3.4. Evolution and homology. . . . . . . . . .
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4.
Newer approaches . . . . . . . . . . . . .
4.1. Molecular/genetic approaches . . .
4.2. Neuroecology . . . . . . . . . . . .
4.3. Neuroinformatics and insect nerve
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5.
Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431
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References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432
1. Introduction
The control of insect behavior by uniquely identified
nerve cells is a vast topic, so any review of the area
must be selective. We will concentrate on cases where
relationships between identified nerve cell properties
and behavior have been convincingly established, or
where experimental data highlight general principles of
sensory processing or motor control. The organization
of this review thus will reflect the spirit recommended
to neuroethologists by Franz Huber (1988) when he
stated, ‘‘Questions of principle should guide our
research.’’
Each type of organism (or model system) tends to
have advantages for answering certain types of neurobiological questions. For example, the crustacean stomatogastric system has provided some of the clearest
information on mechanisms for generating rhythmic
behavior and for the short-term reconfiguration of neural circuitry (e.g. Selverston et al., 1998); while studies
of Mauthner-related startle and escape in fish have
provided a model for the control of episodic behavior
and have bridged the conceptual space between studies
on identified neurons of invertebrates and work on
neural pathways in the brains of vertebrates (Eaton et
al., this issue). The question then becomes, what sort of
general principles have been uncovered by the past 25
years of work on insects? The answer documented
below is that analyses of insect identified neurons have
provided some of the very best data on sensorimotor
integration — tracing information from sensory to
motor networks — collected in such a way that it can
be related to the performance of behavior in intact
organisms.
There is, however, a most important related question:
are there fundamental principles of neurobiology yet to
be uncovered for which insect neuroethology could
provide especially valuable input? The answer to that
question is, we believe, related primarily to the diversity
of insects and the detailed information that has been
obtained about individual cells. First, there is enough
information about specific cells in related insect species
that meaningful comparative studies, and construction
of evolutionary models of neural circuitry, are possible
(see also Murphy, this issue). Second, biochemical and
biophysical details are available for specific cells with
known relationships to organismal behavior; this makes
realistic studies of neural computation possible. We will
highlight material that touches on evolutionary and
computational neurobiology, and in our conclusion we
will offer a brief guide to recently developed resources
for comparing and synthesizing information on identified insect nerve cells; electronic databases and analytic software available on the world wide web
(WWW).
1.1. The identified neuron approach
The idea of a truly ‘identified’ nerve cell means one
which can be uniquely recognized in every member of a
species (see Kandel, 1976, for an especially lucid discussion of the concept). In insect research, cells have been
described as identified under a number of different
circumstances; for example when they were found to
occupy a constant position in a ganglion and so could
be assigned numbers (Cohen and Jacklet, 1967), or
sometimes based purely on invariant physiological
characteristics (see below). However, in insect neurobiology high resolution neuroanatomy is a tradition and
so, typically, anatomical and physiological information
are available simultaneously. In its most compelling
form, an identified nerve cell means a unique cell that
has been anatomically and physiologically characterized, and whose input and output connections are
known (O’Shea and Rowell, 1977).
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
In some cases it has been possible only to assign a
nerve cell to an identifiable class. While it is tempting to
insist that such cells are not truly ‘identified’, the goal
of neuroethology is simply to understand how behavior
can be explained by the characteristics of neurons, so
practicality demands that such cells not be ignored.
Indeed, in some sections of the insect brain, e.g. the
mushroom bodies, there are so many neurons with such
intricate connections that establishing individual identities is problematic, while class identifiability may be
enough to allow insights about sensory coding or motor
programming to be made (e.g. Mizunami et al., 1993;
Strausfeld et al., 1998).
It would be easier to discuss identified neurons across
insect species if there were a consistent and meaningful
scheme for naming or numbering cells. The lack of such
a system makes discussion of even one restricted neural
system cumbersome (for example, see Hennig, 1988,
with reference to well-known cricket auditory interneurons). A three digit system proposed for locust thoracic
cells (Robertson and Pearson, 1983) has gained some
acceptance, and has been adapted to other species (e.g.
Westin et al., 1988). Wide adoption of some guidelines
for assigning tags to cells would also facilitate the
storage of cellular data in electronic formats (Rowell,
1988) — a process which is underway already (see
below).
2. Coding sensory information
2.1. Historical perspecti6e
Almost 35 years ago, Kenneth Roeder published the
first edition of his monograph Nerve Cells and Insect
Behavior (Roeder, 1963, second edition, 1967). In that
volume he summarized general principles underlying
the neural control of behavior using examples from
several insect species. Roeder was quite emphatic that
the importance of insects to behavioral neuroscience
was due to what he usually termed ‘neural parsimony’:
given consistently small body size, this very diverse
group manages well-integrated behavioral responses
with a relatively small number of (sometimes) large
nerve cells (see especially, Roeder, 1959). His monograph was written just before the widespread use of
micropipette recordings and single-cell staining techniques led to the accumulation of a significant body of
data on individually identifiable neurons. Although
Roeder did not use the term Identified Nerve Cells, it
was embodied in his work and our chapter title represents, quite intentionally, an echo from his monograph.
At the same time, Vincent Dethier published a comprehensive summary of insect senses (Dethier, 1963).
Knowledge of primary sensory cells at that time included a considerable amount of cytology, but very
411
little physiology. Since then, studies of individual insect
neurons have played a role in elucidating mechanisms
of sensory transduction, particularly related to olfaction and vision (e.g. Boekhoff et al., 1993; Stengl, 1994;
Hardie and Minke, 1993), and in analyzing stimulus
encoding and adaptation processes in receptors (e.g.
Basarsky and French, 1991; Torkkeli and French,
1995). In some cases, progress has been made on understanding behavior largely with information about the
function of sensory receptors (in particular, see Dethier,
1976), but the greatest insights have come from cases
where a group of truly identifiable interneurons that
process sensory information can be studied along with
organismal behavior. Therefore, we will concentrate on
the interneuron level.
2.2. Operating principles
The descriptions below are organized around several
key concepts that seem to us the main themes that have
emerged from work on identified neurons over the past
several decades. They also are concepts that will be
important foci for future work.
2.2.1. Group/indi6idual differences in central circuitry
In vertebrates, some differences in neural structure
and function have been described that represent sexual
dimorphisms related to behavior. These dimorphisms
have been documented in vertebrates only relatively
recently (Breedlove, 1992). In insects there are profound differences in neuronal organization that are
associated with both sex and social experience.
In honeybee brains, the volume of the olfactory
glomeruli, and the volume occupied by the Kenyon
cells of the mushroom bodies varies systematically depending on whether bees are acting as nurses or foragers (Withers et al., 1993). While the difference
between these two groups is normally accompanied by
an age difference, the anatomical plasticity is not related in any simple way to age, but rather to the
different roles of the individuals in the hive. That
individual experience is important to the configuration
of this part of the insect brain is consistent with studies
on Drosophila indicating that sex and social living
conditions influence mushroom body structure (Heisenberg et al., 1995). Other reports (on crickets) have
shown that in adult mushroom bodies there is a definable cluster of undifferentiated cells that can give rise to
new Kenyon cells under the influence of juvenile hormone (Cayre et al., 1994). This provides a basis for
understanding plasticity in adult insect behavior, especially that related to sex-specific response patterns.
There is ample information about sexual dimorphism
in olfactory processing circuits. Insects respond behaviorally to both plant volatiles and pheromone signals
from conspecifics. In fact, it seems that greater progress
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C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
has been made in understanding the sexually dimorphic
parts of the CNS related to processing pheromones
than to more generalized parts of the olfactory system.
In a number of species, specific glomeruli within the
antennal lobe are individually identifiable, and there is
a male-specific glomerulus for processing information
about female attractants (Rospars, 1983; see review in
Homberg et al., 1989). (Subsequently, it has been reported that individual glomeruli can be uniquely identified in at least one vertebrate (Baier and Korschung,
1994), but the sexual dimorphisms found in insect
olfaction have yet to be reported in vertebrates.) Interneurons of insect antennal lobes (and those projecting into the protocerebrum) are also sexually dimorphic
(Matsumoto and Hildebrand, 1981; Burrows et al.,
1982; Boeckh and Ernst, 1987).
Another place where sexually dimorphic nerve cells
have been reported is the visual system of flies. In both
blowflies and houseflies, males possess some identifiable
visual interneurons which are not present in females,
and some visual interneurons that are found in both
sexes show structural differences between them (Strausfeld, 1980, 1991; see Fig. 1). The particular differences
suggested that the neurons of males selectively process
visual motion information from a region of the retinal
array related to binocular visual space and are important for precision aerial pursuit of a potential mate (a
male-specific behavior). This prediction from anatomical work has been borne-out in subsequent studies with
physiological recording (Gilbert and Strausfeld, 1991),
and it has led to a demonstration that the appropriate
visual information is passed to neck and wing muscula-
Fig. 1. Sexual dimorphism at the gross and cellular level of the Calliphorid visual system. Top panel shows a frontal view of the head of male
(left) and female (right) Calliphora erythrocephalia. Dotted line encloses area of the male-specific ‘acute’ zone which represents that area of visual
space where males detect and follow females. Bottom panel is a drawing of a golgi-impregnated, male-specific interneuron with dendrite positioned
in the lobula so as to receive input from the acute zone. Inset at bottom left shows approximate viewing angle for visual afference to the cell. For
scale, diameter of soma is approximately 25 um. Taken with permission from Strausfeld (1991).
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
ture by a cluster of individually identifiable premotor
interneurons (Gronenberg and Strausfeld, 1991).
Thus there is abundant documentation that sexual
and other group differences are significant both at the
level of the neuropil and tract organization of the CNS,
but also at the level of individual neurons — and
especially in olfactory, visual, and association areas
(mushroom bodies). Such differences are profound in
the sense that they underlie fundamental differences in
sensory capacity between females and males, and they
are an important context within which future information about identified neurons and circuits must be
evaluated.
2.2.2. The importance of temporal dynamics
Rhythms are important to neural function on both
the sensory and motor sides of the CNS. Rhythms
related to identifiable cells have now been reported in
several sensory systems and they occur across time
scales ranging from milliseconds to hours. For example,
far toward the visual periphery, the numbers of
synapses between photoreceptors and optic interneurons, as well as the diameter of interneuron processes,
have been reported to undergo daily fluctuations in flies
(summary in Meinhertzhagen and Pyza, 1996). These
are truly circadian changes that persist when animals
are brought to constant lighting conditions. At a much
finer time scale, the olfactory system processes sensory
information within the context of rhythms.
In locusts, olfactory interneurons that project from
the antennal lobe (the initial CNS relay for olfaction) to
the mushroom bodies encode information on odorant
identity. Each interneuron responds with impulses to
several specific odors or components of odor mixtures,
and for each given odor a slightly different subset of the
interneuron population responds (Laurent and Davidowitz, 1994). The activity of all interneurons responding to any odor are spread out in time and are
superimposed on subthreshold electrical oscillations occuring throughout the cell population at 20 – 30 Hz.
Some cells might respond early in the stimulus-induced
oscillations, others late, or others at various odorantcharacteristic times. Thus the neuronal signature of any
odorant is a time-varying ensemble code that is characterized by oscillatory synchronization of key interneurons (Laurent et al., 1996). In recent work with
honeybees it has been shown that picrotoxin, which
does not alter the odorant specific response of projection neurons but which disrupts oscillatory synchronization, abolishes behavioral discriminations that bees
make between chemically similar compounds without
abolishing discriminations for dissimilar odorants
(Stopfer et al., 1997). This interesting study implicates
oscillatory synchrony in fine sensory discriminations.
The dynamically coded information of the projection
neurons ultimately becomes represented as sequences of
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(small numbers of) activated Kenyon cells within the
very large population of Kenyon cells found in the
mushroom bodies. The Kenyon cell population has
been shown in flies to be necessary for odor based
learning (e.g. deBelle and Heisenberg, 1994). It is currently not known why information about odorants
must be encoded dynamically in time, but presumably it
has to do with some fundamental aspects of olfactory
recognition and learning — and this strategy may be a
general one (see discussion in Laurent, 1996).
Finally, the olfactory system is a place where cells
may show particularly long-lasting temporal variations
in activity patterns. In moths, pheromone related neuronal activity passes through the antennal lobes and
mushroom bodies and ultimately activates brain cells
with axons descending toward thoracic motor centers.
These interneurons often produce trains of impulses
which can outlast stimulus application by tens of seconds to minutes (Olberg, 1983; Kanzaki et al., 1991). In
addition, some uniquely identifiable cells show state-dependent changes in activity: if they have little activity
when a stimulus is applied, they markedly increase their
rate of impulse production; but if an identical stimulus
is applied when they are already active, they show a
stimulus related drop in impulse activity (Olberg, 1983;
Kanzaki et al., 1994; see Fig. 2, top). This state dependency has been called ‘flip-flopping’ — because such
cells have been observed to change firing state spontaneously. There is some evidence that these changes in
firing state correlate with the points at which animals
change direction as they zig-zag up an odor plume
(either walking or flying) toward the source of
pheromone (e.g. Olberg, 1983; see Fig. 2, bottom).
Consistent with the idea that such descending interneurons contain higher-level information to guide flight,
they often are multimodal and respond to visual or
mechanosensory input in addition to olfactory cues (see
any of the papers cited above).
2.2.3. The computational sophistication of indi6idual
neurons
A considerable amount of research has been conducted on processing visual input and the relay of
visual and mechanosensory information toward motor
centers. Such work has highlighted the computational
power that is present at the level of individual nerve
cells. Sensory systems typically display ‘subsystems’ for
processing different stimulus parameters that are biologically important. Thus, information derived from the
insect retina is processed by separate interneuronal
groups for extraction of such features as color and
movement. Identified interneurons of flies have recently
provided clear examples of how both intrinsic biophysical and synaptic features contribute to motion detection
subsystems in vision (see Egelhaaf and Borst, 1993, for
a digestible review). This analysis is particularly satisfy-
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C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
Fig. 2. Temporal dynamics of olfactory interneurons and moth behavior. Top panel displays state dependent response of a single descending
interneuron of Manduca sexta (recorded from the axon in the cervical connective) to puffs of pheromone blend on the ipsilateral antenna. In trace
(a) stimulus caused excitation of the cell from low background activity. Trace (b) demonstrates that the same stimulus caused the cell to decrease
firing if it was already highly active. Scale bar 1s, 40 mV. Bottom panel shows activity profile of descending interneuron from Bombyx mori in
relation to turning behavior. Marker pulses at bottom indicate when puffs of pheromone were applied to one antenna, ipsilateral or contralateral
as indicated. Spike frequency diagram above shows that this descending interneuron fired at a high rate for ipsilateral stimulation and a low rate
for contralateral stimulation. The firing state of the interneuron correlates with antennal position as shown in the schematics, and also with turning
behavior (i.e. the insect turns toward the side with the lower antenna). Bombyx males walk toward a source of female pheromone. Scale bar 10
s, 20 spikes per s. Taken with permission from Kanzaki et al. (1991) (top panel) and Olberg (1983) (bottom panel).
ing since there is a long history of behavioral analysis
of fly vision which led to some of the first formal
models in neural computation (see Reichardt, 1987),
and these are now being given cellular substance.
A set of identified cells, referred to generally as
tangential cells, collects information from local
retinotopic movement detectors and generates signals
that seem to be used in visual orientation tasks. Optomotor stabilization is an orientation response that depends on cells tuned to wide field motion, but this
tuning carries with it an ‘automatic gain control’ so
that the spatial tuning of the cells is sensitive to image
velocity. This feature of tangential cell physiology can
now be explained quantitatively as part of interactions
between excitatory and inhibitory inputs to the dendrites of specific tangential cells that are the basis for
direction selectivity (Borst and Egelhaaf, 1990; Kondoh
et al., 1995; Single et al., 1997). Target orientation is a
separate response that requires different interneuron
characteristics — tuning for small moving objects.
Tangential cells displaying this feature achieve it by
synaptic interactions with other tangential cells (specifically a type which uses GABA to reduce responses to
coherent large field motion-see Fig. 3) (Warzecha et al.,
1993; Egelhaaf et al., 1993). For both of these computations, the fly visual pathway offers the possibility of
directly observing where and how they happen. Many
of the interneurons can be observed with intracellular
recording electrodes, and Ca++ sensitive indicators;
and they can be removed selectively from the circuit by
photoinactivation (Warzecha et al., 1993; Egelhaaf et
al., 1993; Egelhaaf and Borst, 1995; Borst, 1996; and
see Fig. 3). With so much information about one
functional subset of sensory interneurons, it will be
possible to answer questions about the rationale for the
circuit’s design (e.g. see Bialek et al., 1991; Haag and
Borst, 1997).
The visual motion information extracted from the
retinal array ultimately is relayed to motor centers —
primarily, but not exclusively, at thoracic levels — for
guidance of locomotion and other behaviors. Large-caliber interneurons performing this function were actually
the first sensory nerve cells studied as individuals (see
Rowell, 1971 for a review), long before afferent networks in the distal optic neuropils of the brain were
explored physiologically. Now that a number of major
descending visual interneurons have been identified and
characterized, a feature that is common among them is
apparent: they typically display parallel encoding of
information from several receptor types. For example,
studies on dragonflies (Olberg, 1981), several dipteran
species (Bacon and Strausfeld, 1986), and locusts (O’Shea et al., 1974; O’Shea and Rowell, 1977; and see
below) have described individual interneurons that
combine visual afference with mechanosensory signals
from various hairs and proprioceptors on the head, or
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
415
Fig. 3. Dendritic contributions to the computation of visual motion in the fly, Calliphora erythrocephala. Bottom diagram shows the major
branches of a VCH-cell in the right half of the brain. VCH is believed to provide inhibitory input to other tangential cells so as to give rise to
tuning for small object motion. The main arborization on the right is located in the ventral part of the lobula plate, the small arbor on the left
is in the ventrolateral brain. The cell was excited under two different stimulus conditions either by front-to-back motion in the ipsilateral visual
field or back-to-front motion in the contralateral field, after the cell had been injected iontophoretically with the Ca2 + indicator fura-2 [illustrated
schematically, top panel]. Color images in middle panels (128 × 100 pixels; 0.1 s exposure time) indicate the relative change in fluorescence (DF/F,
without background subtraction) at 380-nm excitation wavelength induced by the two types of motion stimuli 7.5 s after stimulus onset. Negative
changes in fluorescence indicate an increase in Ca2 + concentration. Ipsilateral motion leads to Ca2 + accumulation only in the lobula plate arbor.
In contrast, contralateral motion leads to Ca2 + accumulation in both arbors in the lobula plate and ventrolateral brain. Labelled arbors are likely
to be the postsynaptic sites of the corresponding ipsi and contralateral input elements. Taken with permission from Egelhaaf et al. (1993).
associated with the antennae. It is clear that this parallel encoding of visual and mechanosensory information
makes sense for efficient guidance of flight.
At least two significant themes emerged from the
work on descending visual-multimodal interneurons;
one illustrates a concept of sensory organization at the
cellular level better than almost any other example, and
the other raises a cautionary note. First, a group of
cells in locust, the ‘tritocerebral commissural giant’
(TCG) and three descending neurons (DNs), combine
wind and visual information in behaviorally coherent
ways (Bacon and Tyrer, 1978; Griss and Rowell, 1986;
Rowell and Reichert, 1986). They clearly detect not just
visual motion, or wind across the head, but rather
meaningful combinations of directional wind and visual
afference that would be associated with various types of
course deviations during flight (see Reichert, 1989).
This phasic feedback information is then interfaced
with the flight motor to correct perturbations. As such,
these identified interneurons provide one of the clearest
and most elegant examples of single cells as feature
detectors. Second, it is clear that large volumes of
physiological data do not always translate into a satisfying understanding of a cell’s role in natural behavior.
The ‘descending contralateral movement detector’
(DCMD) of locusts is probably the most-studied of any
identified neuron. Its response to visual input was extensively described from extracellular and then from
intracellular recordings, and it was described as responding to small novel objects entering a wide area of
the visual field — presumably to arouse the animal, or
trigger startle behavior such as a jump (e.g. citations
above, or Pearson and O’Shea, 1984). However, in
more recent work, it has become clear that DCMD is
not necessarily tuned to small moving objects, but
rather to objects expanding in the visual field that are
on a collision course (Rind and Simmons, 1992; Simmons and Rind, 1992; Judge and Rind, 1997). This
suggests that DCMD may be involved in crash-avoidance in flying locusts (e.g. Robertson and Reye, 1992;
Gray and Robertson, 1997b) or triggering escape in
stationary locusts (e.g. Holmqvist and Srinivasan, 1991,
for flies), but the humbling truth is that its behavioral
role is still not fully established.
2.2.4. Coarse coding and computational mapping
Many insects possess a wind-sensory system that
maps the world at several levels of the CNS. Its general
function — localization of predators by way of wind
cues — has been appreciated for several decades (e.g.
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C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
Roeder, 1963), but the details of its sensorimotor transactions have been understood only recently. The cerci
are paired sensory appendages found on the abdomen
of insects from more than 10 different orders (Edwards
and Palka, 1991). The types of cercal receptors vary by
species, but most possess at least two types of
mechanoreceptors: filiform hairs that detect wind or
near-field acoustic signals (e.g. Edwards and Reddy,
1986; Boyan and Ball, 1990), and bristle hairs that are
touch-sensory (e.g. Murphey, 1985). The numerous filiform hairs (e.g. about 200 – 2000 per adult cercus, depending on species) are each associated with a primary
sensory neuron that is individually identifiable. This
was initially obvious in cockroach where the hairs line
up along the cercus in ordered columns (Nicklaus,
1965; Dagan and Camhi, 1979), but it was recently
verified to be true in cricket (Landolfa and Jacobs,
1995).
The issues that have been raised about sensory coding by cercal systems are seen dramatically in first
instar cockroach nymphs. They possess only two filiform hairs per cercus. So the best directional information that the set of sensory receptors can display in the
CNS is defined by the spatial tuning of the four hair
afferents (Dagan and Volman, 1982). Basically, each of
the hairs responds mostly to wind puffs from one of the
four quadrants around the animal (Fig. 4). Although
these nymphs possess fewer receptors than adults (by
two orders of magnitude) they display behavioral responses to wind that are at least, as well directed as
those of adults (Dagan and Volman, 1982). Obviously,
this system displays ‘coarse coding’ — the overlap of a
relatively few broadly tuned receptive fields — that is
not inconsistent with accurate spatial resolution at the
behavioral level. The usage of this sort of cercal information in adults involves keeping track of many more
primary afferents and interneurons.
The axons of the receptor cells associated with each
filiform hair enter the terminal ganglion, where they
project to a neuropil, the cercal glomerulus. The particular region of neuropil where a filiform afferent terminates in the adult cricket is based largely upon the
direction of wind to which it is sensitive (Bacon and
Murphey, 1984). High-resolution analysis of the territory occupied by uniquely identified cercal afferent
projections has shown that the sensory field around the
animal is represented as a map of wind directions along
a spiral shaped contour within the cercal glomerulus
(Jacobs and Theunissen, 1996; and see Fig. 5). This is
an elegant example of a sensory pathway displaying
information not by way of topographic projection (position on the cercus does not strictly determine where
afferents project), but rather by functional criteria to
produce a ‘computational map’. The widespread nature
of computational maps has been recognized (Knudsen
et al., 1987).
Fig. 4. Identifiable filiform hair receptors and the encoding of directional wind-sensory information. Data are from first instar nymphs of
the cockroach Periplaneta americana. Top (A): scanning electron
micrograph shows a ventro-caudal view of the abdomen; arrows
point to the two filiform hairs (‘lateral’ and ‘medial’) on the right
cercus. Scale bar is 200 mm. Bottom [B]: polar plots give the average
frequency of action potentials evoked in the sensory neuron associated with each of the four hair receptors as wind was delivered from
different angles in the horizontal plane. Medial and lateral hair
receptor response fields are indicated by dotted and solid lines as
indicated. For scale, maximal response of the medial hair receptors to
standard wind puffs was approximately 300 impulses per sec. Taken
with permission from Dagan and Volman (1982).
Extraction of information from the cercal map of
wind-sensory space is performed by interneurons with
cell bodies in the terminal ganglion. These cells have
dendritic arborizations within the cercal glomerulus,
and large caliber axons ascending the nerve cord. The
axons of these second order cells have the largest
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
417
Fig. 5. Complexity of wind afferent map in adult crickets, and the directional information extracted by ascending ‘giant’ interneurons (GIs). Top
panel (A) displays directional information encoded by two pairs of GIs. Polar plot on right gives response of Left and Right GIs 10 – 3a (dotted
line) and left and right GIs 10–2a (solid line) to winds from different directions in the horizontal plane around the animal. As shown by schematic
illustration at left, distance from the origin is equivalent to spike rate such that the maximal spike rate for each cell is scaled as a value of 1.0
and is at perimeter (for example, maximum average spike rate for 10 – 2a was 20 spikes per s). Bottom (panel B): outline of terminal abdominal
ganglion as seen from a dorsal view. Cubic outline encloses the cercal glomerulus. Clouds of color within the glomerular region represent the
probability density functions for the terminal varicosities of identified primary wind-afferent fibers. The color of each density cloud indicates its
directional tuning with respect to the animal’s body axis (key to directions is shown by inset color wheel, yellow indicates stimuli directed toward
animal’s head. Afferents from each cercus form a continuous hemi-map on one side of the ganglion and the hemi-maps are mirror images of each
other across the midline. Bottom (panel C) shows somata and axons of the identified GIs as seen in dorsal view of terminal ganglion. For clarity,
only GIs on one side of the nerve cord are shown. 8, 9= nerve roots; CN, cercal nerve; for scale, soma of 9 – 3a is approximately 50 um in
diameter. Panel A adapted from Miller et al. (1991), Panel B taken from Jacobs and Theunissen (1996), Panel C adapted from Jacobs and
Murphey (1987); all with permission.
caliber in the CNS; Therefore, they are often called
‘giant interneurons’ (GIs), and they rapidly conduct
impulses from cercal receptors to motor cells located in
the thoracic ganglia. A reconstruction of the set of
identified GIs in adult cricket is displayed in Fig. 5
(cockroaches and other orthopteroid species have similar sets of GIs but the exact number varies in each and
the naming schemes differ). The connectivity between
specific GIs and cercal afferents has been analyzed in
several species (e.g. cricket — Bacon and Murphey,
1984; cockroach — Daley and Camhi, 1988; Hamon et
al., 1994; locust — Boyan and Ball, 1989). While
details are beyond the scope of this chapter, the net
effect of cercal afferent input is to elaborate wind-sensory receptive fields, with most of the GIs responding
to winds only from certain directions around the animal (cockroach - Westin et al., 1977; cricket — Tobias
and Murphey, 1979). How then is information about
wind location represented as it is sent to the thoracic
level (closer to the motor system)?
A recent, careful analysis of information coding by
cricket GIs has revealed an interesting regularity to
their receptive field organization (Miller et al., 1991). A
subgroup of cricket GIs that responds to very low
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velocity winds consists of two bilateral pairs of interneurons. As a group, these four cells have remarkably similar shaped receptive fields, and their points of
peak sensitivity are uniformly distributed at 90° intervals around the horizontal plane. Thus, they monitor
wind from each of the four quadrants around the
animal (Fig. 5), and provide spatial sensory information by way of coarse coding (Theunissen and Miller,
1991) not dissimilar to that which was seen in the first
instar nymph. There are other GIs (and ‘non-giants’ —
Baba et al., 1991) that respond to wind in different
velocity and/or acceleration ranges, so the adult system
covers the full wind environment by having several
subsets of cells that fractionate the spectrum of wind
velocity (or acceleration) each coding basic directional
information (Shimozawa and Kanou, 1984; Miller et
al., 1991). A recent re-analysis of GI wind directionality
in cockroach (Kolton and Camhi, 1995) has shown that
each of two GI subsets (3 pairs of ‘dorsal’ GIs and 3 or
4 ‘ventral’ GIs) carries coarsely coded, parallel information about wind stimulus direction. One reason why
there may be several subsets of GIs coding parallel
spatial information is, as suggested above, to fractionate the spectrum of wind velocities or accelerations.
However, there may be other reasons.
In both cockroach and cricket the GI subgroups
correlate with anatomy; GIs axons that ascend within
ventral tracts of the nerve cord (vGIs) generally are
larger in diameter than those that ascend in dorsal
tracts (dGIs). Therefore, considering that reaction time
is crucial for escape from predators, Camhi and Nolen
(1981) proposed that an escape response triggered by
wind is controlled in a sequential manner; initially by
activity in the vGIs, and only subsequently by the more
slowly conducting dGIs. Another possibility, is that
control by the two GI subgroups differs depending
upon locomotor status. In both cockroach (Daley and
Delcomyn, 1980) and cricket (Kohstall-Schnell and
Gras, 1994) the vGIs are inhibited during walking
(especially during fast walking), whereas dGI responsiveness to wind is enhanced. Therefore, it may be that
vGIs are important for escape in an animal that is
standing or walking very slowly, but that the dGIs are
a channel for evasive responses in an animal that is
already running. Consistent with this idea, vGIs have
been shown to be inhibited during flight, but dGIs
retain sensitivity to wind puffs during flight, and display appropriate motor outputs to participate in evasive responses during flight (Ritzmann et al., 1982;
Libersat, 1992; Ganihar et al., 1994). To arrive at a
better understanding of why there may be multiple
displays of sensory information within the GI system,
as well as other basic questions, comparative studies of
nymphal and adult interneurons might be especially
valuable (both nymphs and adults walk, but only adults
fly).
2.2.5. Neural parsimony and the decoding of central
information
In order for sensory information to become overt
behavior, it must be delivered to a motor system. It is
clear that in many cases — especially those studied so
far in vertebrates — this requires decoding of information that is distributed across a large population of
sensory interneurons. The computational mechanisms
by which this occurs are not yet entirely clear (e.g.
Georgopoulos, 1990; Groh et al., 1997). In insects these
sorts of computations involve especially tractable numbers of interneurons. Two systems where identified
interneurons are known to carry crucial information
that directs motor activity are found in the auditory
system of crickets and the wind and somatosensory
systems of cockroaches.
The auditory system may demonstrate the greatest
neural parsimony of central elements controlling behavior. Acoustic orientation by crickets is a complex behavior and it is species specific (Huber et al., 1989;
Boyan, 1993 are recommended sources of information
on cricket auditory behavior and comparative auditory
organization). Orientation requires that an animal identify and localize a sound source. The relationship between these two processes is controversial and may
differ across the various insect species that use acoustic
communication (e.g. crickets — Pollack, 1986; Stabel
et al., 1989; Doherty, 1991; grasshoppers von Helversen
and von Helversen, 1995). We will restrict our summary
to the processes of sound localization.
When crickets detect a sound, they face the task of
choosing a direction in which to orient: if an acoustic
signal is of low frequency (say 2–15 kHz, and with
appropriate temporal structure for the song of a conspecific) a cricket usually will turn toward the signal
source. The decision about where to turn may follow an
algorithm as simple as ‘turn to the side of the ear more
strongly stimulated’ (e.g. Horseman and Huber, 1994a).
This computation is probably made by cells in the
brain, based on signals received from several key interneurons that send information to the brain from
thoracic auditory areas. In particular, paired ascending
interneurons (usually referred to as AN1) are tuned to
appropriately low frequencies, and faithfully copy the
temporal pattern of a calling song (see Schildberger et
al., 1989). When sound comes from one side, the AN1
on that side is more strongly activated than its contralateral homologue and the animal turns toward the
sound source (the side of the more active AN1). If an
AN1of the cricket Gryllus is hyperpolarized while song
is delivered, animals reverse direction and turn away
from the sound source (toward the side of the originally
less-active AN1) (Schildberger and Hörner, 1988). In
the cricket Acheta, animals with the equivalent cell
unilaterally photoablated have difficulty reaching an
attractive sound source (Atkins et al., 1984); and ani-
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
mals engage in phonotactic circling when the threshold
of this interneuron is lowered unilaterally by application of hormone (Stout et al., 1991).
These sorts of data suggest that the relative level of
activity in the left vs. right AN1 determines turning
direction. A second pair of cells, usually referred to as
AN2, may also contribute, but seem to be less crucial
for localizing calling song than the AN1 pair (see
Horseman and Huber, 1994a,b). The AN2 cells may
also be involved in predator evasion (see below).
Nonetheless, positive phonotaxis can be modeled effectively by assuming a bilateral comparator in the brain
that needs input from only one or two pairs of acoustic
interneurons with broadly tuned spatial receptive fields
(Huber et al., 1984; Pollack, 1986; Stabel et al., 1989;
Horseman and Huber, 1994b). If some temporal filtering functions are added, (their exact placement is not
agreed upon) then this simple comparator might explain the orienting component of important cricket
behaviors such as mate finding, courtship, and intermale aggression.
When presented with high frequency sounds (say
20 – 80 Khz) crickets more typically turn away from a
sound source. This has most often been studied in
flying insects and seems to be the mechanism by which
predators emitting ultrasound (bats) are evaded. While
in flight mode, tethered crickets respond to a source of
ultrasound by producing motor responses that create
contraversive turns (Moiseff et al. 1978). A pair of
identified interneurons (called int-1 in Teleogryllus, and
probably equivalent to AN2 in Gryllus) have been
linked with evasive acoustic behavior. Once again, the
origin of turning ‘commands’ is probably the brain,
based upon inputs received from the pair of ascending
AN2/int-1 cells. Evasive turning can be initiated by
stimulating one member of the interneuron pair electrically (the animal turns away from the active int-1), and
evasive turning from ultrasound can be blocked if this
cell is inactivated (Nolen and Hoy, 1984). A bilateral
comparator might also explain the orientation of this
negative phonotactic response (see Hoy et al., 1989, for
discussion of a general circuit model). Comparative
studies have provided insights here, and may be useful
for work in the future aimed at understanding the
sensorimotor circuitry. For example, mantids contain a
pair of identified ultrasound sensitive interneurons, but
they receive input from a midline ‘cyclopean’ ear, and
are non-directional (Yager and Hoy, 1989). Evasive
responses of flying mantids do not display directionality
(Yager et al., 1990).
The escape system of cockroaches shows some formal similarities to the systems for phonotactic orientation in crickets, but displays control by a larger set of
sensory interneurons, and shows multimodal control by
at least two sensory systems. When a wind puff is
directed at a stationary cockroach (Camhi and Tom,
419
Fig. 6. Thoracic circuitry identified in the cockroach for converting
GI activity into leg movements that orient the escape response. Each
neuronal component in this schematic summary represents a population of bilaterally paired neurons, many of which have been identified
as individuals. For simplicity, only vGIs and TIs with axons on the
right side of the nerve cord are shown. The expanded image of the
metathoracic ganglion (T3) shows details of some established connections. TI, thoracic interneuron (those receiving input from ventral
GIs-as shown-are referred to as ‘type A’: these TIs have ventromedial
(VM) branches, where connections from GIs appear to be made).
Taken from Ritzmann and Pollack (1990) with permission.
1978) or a cricket (Gras et al., 1994; Tauber and
Camhi, 1995) the animal turns away from the direction
of the incident wind, and then runs (crickets also may
jump, and generally display a greater diversity of responses to wind than cockroaches — Baba and Shimozawa, 1997). Abundant evidence links turning
behavior with the wind-sensory GIs that ascend within
the nerve cord (see above) to activate premotor cells in
the thoracic ganglia. In particular, the vGI subset of
cockroach activates identified premotor interneurons
which can support the leg movements that underlie
turning (Ritzmann, 1981; Ritzmann and Pollack 1986;
Ritzmann and Pollack, 1990; Figs. 6 and 7 for an
example of wind-evoked escape behavior). Furthermore, specific enzymatic deletions of vGIs can cause the
direction of escape turns to be altered, indicating that it
is specifically the readout of vGI wind sensory information that is used to orient escape turns (Comer, 1985;
Comer and Dowd, 1993).
The vGI subset consists of four bilaterally paired
interneurons. It is clear that the laterality of a wind puff
is represented by the laterality of GI impulse activity in
the CNS (e.g. Westin et al., 1977; Smith et al., 1991).
Wind from one side of the animal activates the vGIs on
the ipsilateral side of the nerve cord more strongly, and
also at a shorter latency, than those on the contralateral
side. The algorithm for choosing a turn direction may
be as simple as ‘turn away from the side on which the
vGIs display more wind-evoked activity’ (for example,
see Dowd and Comer, 1988; Camhi and Levy, 1989).
Consistent with this notion, specific unilateral vGI lesions can cause animals to turn inappropriately toward
winds delivered from the same side as the lesion (see
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Comer and Dowd, 1993). More specifically, experimental tests with selective GI stimulation have shown that
timing (relative latency) of GI activity cannot explain
turn direction, but relative levels of spiking in left vs.
right GIs can explain it (Liebenthal et al., 1994). It is
far less clear how a specific turn angle is chosen,
although it may depend upon the relative level of
activity among the GIs with differing directional sensitivities (e.g. Levi and Camhi, 1994).
The thoracic motor circuitry of cockroaches operates
as if it compares not only the relative level of activity in
the bilateral pairs of GIs, but also in other interneuron
systems. When the GIs are completely removed, escape
can be elicited via tactile sensory pathways that descend
to thoracic ganglia from the antennae (Comer et al.,
1988; Burdohan and Comer, 1990; Stierle et al., 1994).
This is not a question of functional substitution; rather,
this one particular motor response is normally under
Fig. 7. Multiple GI systems can explain initiation of directed escape movements by cockroaches in response to multiple sensory modalities and
types of predators. Panels on left show reconstructions of escape responses from videotapes, Panel on right shows two identified ‘giant’
interneurons that are involved in the control of these types of responses. (A) Escape triggered by the attack of a wolf spider (red). Spider’s position
prior to movement is labeled 0, front of spider’s body and right foreleg are shown on frames 4 and 8. Cockroach (black) began turning between
frames 8 and 9 subsequent to antennal contact. (B) Escape triggered by strike of a marine toad (green). Toad’s initial position is labeled 0, front
of toad’s body and position of tongue (dotted outline) are shown on subsequent frames as noted. Cockroach (black) began moving between frames
4 and 5 prior to contact and tongue extension. Scale bar = 2 cm and it applies to panels A and B. (C) Reconstructions of two cells injected with
cobalt hexamine. Descending mechanosensory interneuron (right DMIa-1; red) responds to antennal contact. Its activity, and that of other DMIs,
is correlated with direction and angle of escape turns evoked by antennal contact (such as A; see Ye and Comer, 1996). DMIs have the largest
caliber descending axons in the nerve cord, and are similar in size to the classic giants. Ascending giant interneuron (left GI-1; green) responds
to wind. Its activity, and that of other ventral GIs, is necessary for properly oriented escape turns to wind stimuli (such as B, see for example
Liebenthal et al., 1994; Comer and Dowd, 1993). Scale bar = 100 um. Panel A & B taken from Comer et al. (1994), panel C from Comer and
Dowd (1993).
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
multisensory control. It is now known that there is a
larger somatosensory system involved in eliciting escape
(Comer et al., 1988; Ritzmann et al., 1991; Comer et
al., 1994; Pollack et al., 1995). Studies with real predators indicate that under normal circumstances the windsensory pathway for escape is important to detection of
large predators that generate wind cues by their movements (Camhi et al., 1978). However, the antennal
touch-sensory system is particularly important for detecting smaller predators — such as spiders and other
insects — that are able to approach cockroaches
closely (Comer et al., 1994; Fig. 7). The system for
touch evoked escape displays at least some of the
cellular principles that have already been defined for
the wind-sensory system.
Two bilateral pairs of interneurons carrying antennal
touch-sensory information from the head ganglia to the
thoracic ganglia have been identified (Burdohan and
Comer, 1990, 1996) and they have been called Descending Mechanosensory Interneurons (DMIs). Together
they possess the largest diameter axons in the cervical
connectives, making them, in effect, descending ‘giants’
(Fig. 7). Touching one antenna activates the DMIs on
the contralateral side of the nerve cord more strongly,
and also at a shorter latency, than those on the ipsilateral side. So an algorithm that might explain touch
evoked escape would be ‘turn toward the side of the
DMIs with more, or earlier, touch-evoked neural activity’. Section of a cervical connective to block this
system unilaterally causes animals to turn typically
toward, rather than away from, abrupt stimuli touching
the antenna contralateral to the lesion (Comer et al.,
1994) — supporting the idea that the relative level of
DMI activity might determine turn direction. This notion has been tested more directly, since the DMIs can
be recorded in behaving animals during the performance of escape turns (Ye and Comer, 1996). These
recordings have revealed that the relative number of
impulses in the DMIs on each side of the CNS, not
their relative timing, is correlated with the direction,
and specific angle, of escape turns evoked by touching
an antenna (Ye and Comer, 1996). This makes a bilateral comparator model particularly suitable for explaining DMI control of escape.
While it is clear that the thoracic premotor interneurons previously shown to receive wind-sensory vGI
input also receive some tactile sensory input (Ritzmann
et al., 1991; Ritzmann and Pollack, 1994), the interneurons providing that input have not yet been determined.
If the DMIs and GIs converge upon the exact same
thoracic premotor cells, then there will be some interesting issues raised about coordination between tactile
and wind-sensory control of behavior; The DMI pathway represents antennal touch information with a contralateral bias, and animals turn ipsiversively with
respect to the most active DMIs; however the GI
421
pathway displays an ipsilateral bias and animals turn
contraversively with respect to the side of the more
active GIs (see Comer et al., 1994). Thus if there is one
readout network for both the ascending (GI) and descending (DMI) system, then it must switch its operating logic depending upon which input it is handling.
This remains to be determined, but the DMIs and GIs
should provide some interesting tests of movement
control by very small populations of sensory interneurons and of principles underlying multisensory
integration.
3. Patterning of motor output
3.1. Historical perspecti6e
The investigation of the role of identified neurons in
generating the motor patterns that underlie behavior of
insects has a history at least as long as that of the
general field of the neuronal control of behavior. This
history has been recounted, at least in part, in several
reviews (e.g. Hoyle, 1983; Robertson, 1987b; see also
Burrows, 1996) and is not the major focus of the
current chapter. Suffice it to say that soon after the
development of intracellular microelectrode techniques,
recordings were taken from insect motor neurons (e.g.
Hagiwara and Watanabe, 1956) and some of the first
intracellular recordings taken from neurons during expression of behavior in any organism were made from
cricket neurons during singing and the generation of
flight-like rhythms (Bentley, 1969a,b).
The original difficulty with this approach was the
problem of identification which was only truly solved
with the development and sophistication of intracellular
staining techniques (e.g. Pitman et al., 1972; Stewart,
1978). Nevertheless, neurons can be identified by their
physiological characteristics alone (e.g. much of the
work on the stomatogastric system of lobsters, Selverston et al., 1998). In insects this approach limits the
investigator, for the most part, to intracellular studies
of motor neurons which can be identified according to
the muscle they innervate; an identification made easier
with invertebrates because of the low number of motor
neurons innervating each muscle. Significant in this
regard was the pioneering work of Hoyle and Burrows
(1973) on the motor supply of the locust hindleg which
laid the groundwork for intracellular investigations of
the control of the locust jump (Heitler and Burrows,
1977a,b) and a rich subsequent literature on the control
of jumping, walking and posture in locusts (see Burrows, 1996). In a similar fashion, for the control of
locust flight, detailed descriptions of the motor patterns
(Wilson and Weis-Fogh, 1962) were followed by intracellular recordings from motor neurons identified on
physiological grounds (Kendig, 1968) and subsequent
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anatomical descriptions of the motor neurons (Bentley,
1970; Burrows, 1973; Tyrer and Altman, 1974). Although information on the firing pattern of interneurons could be inferred from motor neuron recordings
(see Burrows, 1977), in both cases the premotor circuitry remained a mystery until techniques improved
such that interneurons could be identified reliably and
with relative ease (e.g. for flight, Robertson and Pearson, 1982, 1983).
3.2. Operating principles
We have chosen to illustrate the following discussion
with particular but not exclusive reference to the locomotion of locusts (including walking, jumping and
flight) simply because the study of these motor activities
is well advanced and features prominently in the literature. We note in passing, however, that rivals for
attention are increasing in number. As one example,
insect stridulation is a behavior that has a respectable
pedigree in the field of neuroethology (see Elsner and
Popov, 1978; Elsner, 1983; von Helversen and von
Helversen, 1994; for reviews). It also exhibits characteristics that lend themselves to an identified neuron approach in that the behavior is rhythmic, primarily
generated by central circuits, and resistant to the manipulations necessary to enable intracellular recording.
Numerous interneurons in different species of grasshopper have now been described that are involved in
stridulatory rhythm generation (Hedwig, 1986a,b,
1992a,b, 1994; Gramoll and Elsner, 1987; Lins and
Lakes-Harlan, 1994; Ocker and Hedwig, 1996). Similarly, interneurons involved in cricket stridulation,
which is generated by rhythmical movements of the
wings rather than the legs, as is the case in grasshoppers, have been described (Hennig, 1990a,b; Otto and
Hennig, 1993). A safe prediction would be that more
detailed descriptions of neuronal mechanisms underlying this motor activity will not be long in appearing.
3.2.1. Defined circuits
One of the primary advantages of the identified
neuron approach is the ability to accumulate data from
the same neurons and synapses in different individuals
and thus to reconstruct faithfully a circuit that explains
how information is coded and/or how motor patterns
are generated. For motor patterning in a variety of
invertebrates there has been considerable success in this
venture (see Selverston et al., 1998; Calabrese and De
Schutter, 1992; Marder and Calabrese, 1996) because
the circuits contain few neurons and one can be confident that most, if not all, of the neuronal participants
have been identified. In vertebrates the notable success
has been necessarily confined, in all but a few cases (see
e.g. Eaton, this issue; Buchanan, ibid), to the investigation of classes of neurons (e.g. Grillner et al., 1991;
Grillner, 1996; Marder and Calabrese, 1996). In insects
the success in defining completely circuits that generate
motor patterns is limited. For a detailed review of the
neuronal and circuit properties underlying insect motor
activity there is a recent book that does the topic justice
(Burrows, 1996). If there is a single message to be taken
from this review, with reference to insect motor pattern
generation, it is that a focus on single identified neurons
and attempts to ascribe to them particular functions,
has yielded little of lasting value. The original, apparently complete, descriptions of the locust jumping circuit (see e.g. Pearson and O’Shea, 1984) were flawed
(Gynther and Pearson, 1986, 1989). The description for
the locust flight circuit (Robertson and Pearson,
1985a,b; Robertson, 1986) is woefully incomplete, even
though a computer model of the known circuit can
produce rhythmic activity similar to the deafferented
flight rhythm (Grimm and Sauer, 1995). Moreover
there have been no significant additions to this circuit
since its first description, and there is little information
on the possible role of local neurons. The latter is
deemed surprising (Burrows, 1996) given the prominent
role of local interneurons in the control of insect leg
movements (e.g. for walking, Pearson and Fourtner,
1975; Schmitz et al., 1991; Wolf and Laurent, 1994;
Büschges et al., 1994; Büschges, 1995; Wolf and
Büschges, 1995). There may be good reasons, functional or otherwise, for a negligible role for local interneurons in the control of wing movements, but until
our knowledge of the circuitry is improved no conclusions can be drawn.
The complexity of motor circuits and the allure of
identified neurons (Robertson, 1989) may to some extent explain the failure to define completely a unitary
circuit for any insect motor act. The problem may also
lie in the fact that the circuits fall in the middle of a
continuum of complexity from the stomatogastric system to the lamprey swimming system, and probably
closer to the lamprey. For the locust flight circuit,
interneurons can be individually identified, encouraging
a circuit-breaking approach, yet the numbers of neurons involved is several hundred at a very conservative
estimate. There are around 80 motor neurons for the
wing muscles. A partial catalog of only flight steering
neurons in the mesothoracic ganglion contains 28 neurons (Rowell and Reichert, 1991). Unpublished catalogs in several laboratories contain descriptions of
many more interneurons whose activity is modulated
with the flight rhythm. Finally, circuit-breaking by
paired intracellular recordings from the neuropil segments of flight neurons is time-consuming, difficult and
to a large extent arbitrary.
The successes in insect locomotor systems lie, therefore, not with defined circuits of identified neurons
which, even if complete, would be idiosyncratic
(Robertson, 1989), but with more general concepts (e.g.
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
for flight, Robertson, 1995) that are developed, or are
supported, using the identified neurons as tools to gain
access to the systems. The current areas of most intense
research interest in motor systems include the organization and reconfiguration of functional circuitry, the role
and mechanisms of neuromodulation and the afferent
regulation of output (Kristan, 1992; Pearson, 1993;
Morton and Chiel, 1994; Katz, 1996).
3.2.2. Organization and reconfiguration
The locust flight system has been described as a
single operating circuit with neuronal elements distributed throughout six serially homologous, segmental
neuromeres (Robertson et al., 1982). Outputs from this
single central source of rhythmicity are thought to
activate the sets of motor neurons for all four of the
wings. The evidence for this is based on the location
and postsynaptic targets of interneurons that drive
motor neurons and of those that can be shown to
participate in rhythm generation using standard tests
(Robertson and Pearson, 1983). There is very little
evidence of strict serial homology of patterning elements and experiments to surgically isolate sections of
the ventral nerve cord have supported the original
description (Wolf and Pearson, 1987a). It is important
to note that whereas the central element is conceived of
as a rhythmical unit driving the motor neurons for all
four wings this does not indicate a single mechanism
for rhythm generation in the system. Indeed, there is
evidence for multiple oscillatory mechanisms and stimulation of different single interneurons recruits different
subsets of interneurons to evoke flight-like rhythms
with different characteristics (Robertson, 1987b). The
point is that there is currently no evidence for wing-specific oscillators in the deafferented system.
This type of organization is unusual for a motor
system that controls bilateral and serially repeated appendages. Such systems are most often organized as
coupled central oscillators (see Marder and Calabrese,
1996, for a discussion of segmental oscillators in lamprey and leech swimming). In insects, hemisegmental
oscillators (or unit burst generators, Grillner, 1985)
have been demonstrated or suggested for locust walking
(Ryckebusch and Laurent, 1994), locust grooming
(Berkowitz and Laurent, 1996), stick insect walking
(Bässler, 1993), grasshopper stridulation (Ronacher,
1989), and tymbal sound production by tiger moths
(Dawson, 1995).
The most telling pieces of evidence against the adequacy of the described flight circuitry and its interpretation have come from lesion experiments designed to
demonstrate the existence of hemiganglionic oscillators
for wingbeating (Ronacher et al., 1988; Wolf et al.,
1988), though these were unable to demonstrate conclusively the existence of hemiganglionic oscillators (Wolf
et al., 1988). In animals with afferents intact, flight and
423
rhythmical motor activity are remarkably resistant to
longitudinal hemisections of the segmental ganglia that
would damage the interneurons in the described flight
circuit. This is true also for deafferented preparations
under the influence of octopamine. Most interestingly, a
mesothoracic hemiganglion separated from the majority
of the flight circuitry by hemisection and transection of
the ipsilateral meso-metathoracic connective could still
produce robust rhythmical motor neuron activity to
muscle 99 though this was poorly coordinated with the
rhythm supplying other wing muscles (Ronacher et al.,
1988; Fig. 8). The rhythmical drive for this motor
neuron could be derived from a putative hemiganglionic oscillator, however it could also be a result of
timing information from residual flight circuitry being
relayed through the prothoracic ganglion and down the
intact pro-mesothoracic connective. A further possibility is that the intact afferents serve to generate the
rhythm in a chain-reflex fashion.
Nevertheless, there is very suggestive evidence for
hemiganglionic oscillators in flight preparations prior to
deafferentation but relatively weak evidence after deafferentation (Figs. 8 and 9). It is an interesting possibility that the capacity for the flight circuit to be
reconfigured by proprioceptive input (Wolf and Pearson, 1989) and the short-term plasticity evident during
tethered flight (Möhl, 1988, 1993) are sufficient to
enable a profound reorganization such that wing-specific oscillators are created by the afferent input. Flight
rhythms produced by deafferented preparations in the
absence of octopamine rarely last longer than a few
seconds. Thus the central component (the unitary oscillator of the deafferented preparation) could act like a
starter motor that is then converted to the running
engine (putative coupled oscillators of the intact system) by proprioceptive feedback.
The relevance of the described flight circuitry
(derived from deafferented preparations) for generation
of the motor patterns of intact animals is also challenged by information on the reconfiguration of the
flight circuit by proprioceptive afferents. A significant
advance in the investigation of the neuronal control of
flight was the development of a preparation with which
it is possible to record from neuronal somata of tethered flying locusts that have intact afferents (Wolf and
Pearson, 1987b). This preparation involves tethering
the locust upside down and has drawn criticism for this
reason (Stevenson and Kutsch, 1987). However, the
original findings and interpretations have been confirmed by making electromyographic and intracellular
recordings of the motor activity before and after inversion of the same preparation (Pearson and Wolf, 1989).
With this preparation it became feasible to investigate
directly the contribution made by afferent input in the
generation of the rhythm. Several interneurons with
weak, and often subthreshold, membrane potential os-
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Fig. 8. Rhythmical motor activity recorded from hemisected mesothoracic ganglion in the locust. (A) Electromyographic activity recorded from
right and left forewing depressor muscles (M97r and M99l) and from a left hindwing depressor (M128l) in a preparation with a hemisected
mesothroacic ganglion and a transected left meso-metathoracic connective. Note the generation of a flight-like rhythm although there is poor
coordination between M99l and the other wing muscles. (B) Rhythmical membrane potential oscillations recorded intracellularly from an elevator
motor neuron in a deafferented preparation after the application of octopamine. Note that only two cycles are recorded and the presumed rhythm
has a very low frequency. Taken with permission from Ronacher et al. (1988) and Wolf et al. (1988)
cillations in deafferented preparations showed robust
bursting activity in intact animals, and at least one
interneuron behaved in the opposite fashion with bursting activity in deafferented preparations but weak oscillations in intact animals (Wolf and Pearson, 1989).
These results indicate that the circuit of active interneurons is different in deafferented and intact animals.
This is to say that afferent input reconfigures the circuit
rather than simply regulating the timing of elements
within a central pattern generator that can be considered as a defined unit (or black box) contributing to the
output. Similar results have been seen in the ventilatory
system of locusts in which the number of active neuronal elements is dependent upon the vigor of ventilation with more elements recruited as ventilation
becomes more vigorous (Ramirez and Pearson, 1989).
Another aspect of reconfiguration concerns the circuitry for the control of appendages (or muscle sets)
that are used in different motor acts (Morton and
Chiel, 1994). Interneurons can be dedicated to function
in the production of a particular behavior or they can
participate in the production of more than one. In
insects it has been possible to identify both types of
organization: interneurons for the control of bifunctional leg muscles seem to be divided into separate sets
for the production of flight or walking of locusts
(Ramirez and Pearson, 1988) and flight or stridulation
of crickets (Hennig, 1990a), whereas other neurons can
participate in the control of both ventilation and flight
in locusts (Ramirez and Pearson, 1989) ventilation and
stridulation in locusts (Otto and Hennig, 1993) and leg
and wing stridulation in grasshoppers (Elsner, 1974). A
mechanism underlying the reconfiguration of circuits
controlling two behaviors has recently been described in
locusts (Jellema and Heitler, 1997). The different behaviors (kicking and thrusting of the hindleg) are differentially characterized by the flexion angle of the tibia
when they occur (full-flexed for kicking; around 90%
for thrusting). Interestingly, it is feedback from the
femoral chordotonal organ monitoring this joint angle
that mediates the reconfiguration by biasing synaptic
connections. During thrusting of the leg this afferent
activity modulates the gain of connections from strain
detectors in the leg cuticle and central connections
between leg motor neurons to prevent the co-activation
of flexors and extensors that is necessary to produce the
stored energy required for a kick.
3.2.3. Neuromodulation
It is now the accepted wisdom that pattern generating circuitry is under continuous modulatory control
via neuroactive substances circulating in the blood (or
hemolymph) or released locally in the neuropil (for
reviews see Harris-Warrick and Marder, 1991; Pearson,
1993). The functions of such neuromodulation include
activating and/or priming the circuitry, and reconfiguring the ensemble of participating neurons.
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
Insect nervous tissue contains the usual complex
cocktail of neuroactive substances (e.g. Nässel, 1996)
that have been localized in specific neurons using immunocytochemical techniques. It is to be anticipated
that these have roles to play in controlling the flexibility
of motor pattern generators, among other things. In
most cases the studies have progressed little further
than identification and detailed description of immunoreactive neurons (most useful for comparison
across taxa). With reference to motor patterning, most
information about neuromodulation concerns octopamine which has multiple roles as a transmitter, neuromodulator and hormone (see Orchard et al., 1993).
Octopamine is widespread in insect nervous systems
(Stevenson and Spörhase-Eichmann, 1995). There has
been considerable interest in the role of octopamine
ever since the suggestion by Hoyle (1975) and confirmation by Evans and O’Shea (1978) that dorsal unpaired
median (DUM) neurons used octopamine as a transmitter to modulate a myogenic rhythm of the extensor
tibiae muscle of the locust leg. More recently it has
been demonstrated that the injection of octopamine
into specific regions of flight neuropil in the metathoracic ganglion can activate flight rhythms (Sombati and
Hoyle, 1984; Stevenson and Kutsch, 1987). The mechanism underlying this is likely to be the induction of
plateau potentials in specific locust flight interneurons
(Ramirez and Pearson, 1991a,b) in combination with a
more generalized arousal of the system (Fig. 9). Octopamine has also been shown to modulate the spiking
activity of a wing-hinge proprioceptor in the locust
(Ramirez and Orchard, 1990), a leg proprioceptor in
stick insects and locusts (Ramirez et al., 1993;
425
Matheson, 1997), synaptic interactions between interneurons in escape circuitry of the cockroach
(Casagrand and Ritzmann, 1992), leg motor neurons in
locusts (Parker, 1996) and flight muscle in locust
(Whim and Evans, 1991; Stevenson and Meuser, 1997)
indicating a modulatory role throughout the behavioral
machine from sensory neurons through to muscle. It
has been suggested that the local release of octopamine
by specific subsets of DUM neurons could tune particular regions of neuropil for the generation of different
behaviors (the orchestration hypothesis, Sombati and
Hoyle, 1984; reviewed in Bicker and Menzel, 1989;
Burrows, 1996) though there is currently little evidence
for such an overarching coordinating role (but see
Burrows and Pflüger, 1995). Alternatively there are
suggestions that octopamine mediates arousal. This interpretation is supported by the observation that octopamine dishabituates the response of DCMD when it is
released from identified octopamine immunoreactive
neurons in the optic lobe of locusts (Bacon et al., 1995).
In addition, olfactory reward learning in honeybees is
enhanced by octopamine and depolarization of an identified ventral unpaired neuron (VUMmx1, containing
octopamine) can substitute for the reward in olfactory
conditioning (see Hammer, 1997).
The role of acetylcholine as a neuromodulator acting
via an influence on muscarinic receptors is a topic with
some relevance to the control of motor activity (for
review see Trimmer, 1995). The application of pilocarpine (or other muscarinic agonists) to several different insect preparations can induce the generation of
rhythmical motor patterns (walking — Ryckebusch
and Laurent, 1993; Büschges et al., 1995; stridulation —
Fig. 9. Hyperexcitability of the flight rhythm generated by locust thoracic ganglia after perfusion with octopamine. [Ai] Structure of identified
flight interneuron 401. [Aii] Brief electrical stimulation (STIM) of interneuron 401 induces a flight- like rhythm recorded intracellularly in 401 and
a contralateral depressor motor neuron (DEP). [B] Injection of a long pulse of depolarizing current into interneuron 566 evokes tonic activity
before (i) and flight like rhythmical activity after (ii) the application of octopamine. Taken with permission from Ramirez and Pearson, 1991a and
Ramirez and Pearson, 1991b.
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Heinrich et al., 1997; see also Trimmer, 1995; crawling
in moth larvae — Johnston and Levine, 1996;
mandibular rhythms — Rast and Bräunig, 1997).
Much of this effect is likely attributable to the increase
in excitability of identified neurons when treated with
muscarinic agonists (Trimmer, 1994; David and Pitman, 1995, 1996), but other effects cannot be ruled out.
Indeed, it has been demonstrated that muscarinic antagonists can augment the amplitude of postsynaptic
potentials from the forewing stretch receptor in locusts
and it is proposed that this represents self-regulation at
this cholinergic synapse via presynaptic autoreceptors
(Leitch and Pitman, 1995). The studies on the activation of motor patterns require the usual caveats for
investigations of neuromodulatory agents given that it
is difficult to know precisely what the treatment is
doing. The problems of interpretation are only slightly
mitigated when the agents are injected into neuropil
(e.g. Heinrich et al., 1997) rather than bath-applied (e.g.
Ryckebusch and Laurent, 1993). In spite of this, the
ability to induce walking motor rhythms in reduced
preparations of insects is a significant advance and will
allow the control of this behavior to be addressed by
the identified neuron approach.
tic terminals of the afferent reduces the efficacy of
action potentials in the afferent and this can serve a
variety of important functions such as to prevent saturation in the pathway (Burrows and Matheson, 1994),
to reduce hysteresis (Hatsopoulos et al., 1995) and to
mitigate the effects of reafference (Hedwig and Burrows, 1996). During the generation of motor patterns
for locomotion, rhythmical presynaptic inhibition of
proprioceptive afferents occurs to modulate phase-dependent reflexes and, whereas other afferents may be
involved, it is clear that centrally generated signals must
contribute to the presynaptic depolarization (Wolf and
Burrows, 1995). An interesting idea has been proposed
that a general role for presynaptic inhibition is to
reduce the efficacy of feedback from predicted movements (predicted by the output of a central pattern
generator) and to allow the feedback from unexpected
perturbations of movement to have greater efficacy
(Burrows, 1996). Presynaptic inhibition is a critical
process for efficient motor control (in humans the
modulation of presynaptic inhibition is affected by the
defects associated with spasticity — Stein, 1995). This
process is eminently accessible to cellular investigations
in insect motor systems.
3.2.4. Afferent regulation
Motor patterns in insects are generated by a combination of central and peripheral mechanisms. The futile
(in retrospect) debate over the relative contribution of
each to pattern generation is largely over and the focus
has rightly shifted to a consideration of the precise role
of proprioceptive feedback in structuring the pattern.
From research with the locust flight system it is evident
that proprioceptive input can reconfigure the central
circuit of active neurons (see above). Notwithstanding
this there is strong evidence that rhythmic proprioceptive discharges have a prime role in regulating the
timing of phase transitions and that this role is mirrored in other systems including those of vertebrates
(see Pearson, 1993, 1995). In the locust flight system
wing-hinge stretch receptors monitor elevations and the
tegulae monitor depressions. Activity of the stretch
receptors promotes an earlier onset of depressor motor
neuron bursts by reducing the degree of hyperpolarization between bursts, whereas activity of the tegulae
recruits elevator interneurons and advances the timing
of elevator motor neuron bursts (see Pearson and
Ramirez, 1992). Other proprioceptors exist and are
likely to be involved in patterning the motor output but
their roles are incompletely understood at present (e.g.
Pearson et al., 1989; Stevenson, 1997).
The gain of the connections made by afferents with
identified postsynaptic targets is under presynaptic inhibitory control (cricket — Levine and Murphey, 1980;
locust — Pearson and Goodman, 1981; cockroach —
Hue and Callec, 1983). Depolarization of the presynap-
3.3. Neuronal restructuring
The ability to investigate the properties of identified
neurons and identified synapses has considerable advantages for investigations of structural plasticity and
its consequences in the nervous system. Such changes
are associated with long-term reorganization of circuits
during the processes of maturation, metamorphosis,
learning and memory, and recovery from peripheral
injury. Insects have provided several model systems
with which to pursue an understanding of these phenomena (e.g. Weeks et al., 1997; Kämper and Murphey, 1994) and it is now accepted that results from
these systems have general relevance across different
taxa [including vertebrates] (Murphey, 1986).
An interesting feature of the development and maturation of insects is that they exhibit age-specific behaviors associated with new or modified body parts and
new control circuits. For example the hemimetabolous
locust attains wings only at the final molt and, although
flight rhythms can be generated immediately after molting, it takes two weeks of maturation before an animal
is able to generate a flight motor pattern that has an
appropriate frequency to sustain lift and thrust (see
Kutsch, 1989). The phenomena underlying this increase
in wingbeat frequency are incompletely understood for
it has proved resistant to a variety of experimental
manipulations. There is a correlation with growth of
identified interneurons although several parameters of
identified synaptic potentials in these interneurons are
unaffected by maturation (Gee and Robertson, 1994).
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
The axonal branching of the stretch receptor afferent
shows heteromorphic growth (Gray and Robertson,
1996). The conduction velocity and sensitivity of the
stretch receptor increases (Gray and Robertson, 1994)
and its ability to modify an ongoing flight rhythm
increases (Gray and Robertson, 1997a). These changes
seem independent of the levels of circulating ecdysone
(Kutsch, 1989) or of juvenile hormone although this
may have a role in the switch from a larval rhythm to
the adult rhythm (Kutsch and Stevenson, 1984). During
larval development other identified afferents in this
system show structural alterations indicating changes in
connectivity and this change is activity-dependent, not
hormonally-mediated, and likely to be regulated by
competition for synaptic sites (Pflüger et al., 1994). The
evidence suggests that the flight circuitry is in a dynamic state throughout maturation and able to react to
external influences so that the final form of the circuit is
appropriate for an individual’s morphology.
That the profoundly dynamic nature of the locust
flight circuit continues into the fully mature adult stage
is well demonstrated by the effect of peripheral lesions
on the system. The tegulae are proprioceptors that
monitor wing position during the downstroke and have
a significant role in the generation of aerodynamically
appropriate wing movements (Wolf, 1993). Removal of
the hindwing tegulae causes a reduction in wingbeat
frequency that recovers during the subsequent 2 weeks
(Büschges and Pearson, 1991) although in most animals
the recovery is not sufficient to enable free flight (Gee
and Robertson, 1997). This recovery appears to be
mediated by growth of the forewing tegulae afferents
and the formation of new and more reliable connections with flight interneurons (Büschges et al., 1992a,b).
The extent and speed of recovery are not affected by
the maturational stage (post imaginal molt) of the
animal (Gee and Robertson, 1996). Interestingly, removal of a single hindwing tegula (which projects to
only one side of the thoracic ganglia) has structural and
synaptic consequences for connections on the contralateral (uninjured) side indicating a role for retrograde
signals and competitive interactions in the restructuring
of these afferent pathways (Wolf and Büschges, 1997;
Fig. 10).
These changes are reminiscent of the competitive
interactions and retrograde signals that have been elegantly demonstrated in the cercal system of crickets
during development (Murphey and Lemere, 1984;
Chiba et al., 1988; Davis and Murphey, 1994a,b; Murphey and Davis, 1994). It is becoming quite clear from
these studies that insect circuits are in a dynamic state,
continually able to restructure neurites and modify
synaptic connectivity as a consequence of competitive
interactions. This occurs throughout development and
is retained into adult stages. The similarity with mammalian systems (Kaas, 1991) is striking and perhaps
427
Fig. 10. Diagrams illustrating the change in connectivity from tegulae
afferents onto a generalized flight interneuron in the metathoracic
ganglion after recovery from unilateral hindwing tegula removal. (A)
shows normal organization and site of lesion (scissors). Lines traveling down the meso metathoracic connectives indicate afferents from
the forewing tegulae. One synapse from an afferent to an interneuron
indicates 10% frequency of connection and the size of the terminals
indicate the strength of the connections as measured by the amplitude
of compound EPSPs. Note that the reliability and strength of connections from the forewing tegula on the side ipsilateral to the lesion
increase (B), and that there is also a retrograde effect on the connections from the forewing tegula on the side opposite the lesion. Taken
with permission from Wolf and Büschges (1997).
insects will afford one of the best opportunities to study
the underlying mechanisms of this form of plasticity at
the level of identified neurons.
A more dramatic restructuring of motor circuits occurs during the metamorphosis of holometabolous insects such as Manduca and Drosophila (Truman, 1990;
Weeks and Levine, 1992; Levine et al., 1995). In these
systems the peripheral structures change dramatically to
support a completely different life-style: a nervous system appropriate for larvae requires disassembly prior to
construction of a nervous system appropriate for
adults. The processes that are involved in this transformation include targeted cell death, postembryonic neurogenesis, restructuring of neuronal arbors, synaptic
remodeling, and changes in the expression of voltagedependent currents of identified neurons. One of the
most significant features of this research is that the
remodeling is under the control of steroid hormones
allowing a fine-grained, cellular investigation of important phenomena that may be difficult to address at this
level in mammalian systems. This is a well-advanced
field that has been amply reviewed in recent years
(Weeks and Levine, 1995; Levine et al., 1995; Kent et
al., 1995; Weeks and Wood, 1996; Weeks et al., 1997).
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3.4. E6olution and homology
It is a truism to say that the current design of
circuitry is a result of inexorable evolutionary mechanisms acting on pre-existing circuits to adapt them such
that organisms are better suited to their particular
niches. The effects of evolution are clearly recognizable
in the nervous system (Arbas et al., 1991). The consequences of evolution in the context of this article are at
least two-fold. First, current design may owe more to
history than to functional necessity and this complicates the interpretation of the neuroethological utility
of specific features of neuronal structure and operation.
This point has been made previously (Dumont and
Robertson, 1986) and need not concern us further here.
Second, closely related species are likely to be more
similar in the characteristics of their identified neurons
than more distantly related species. This being true it
should be possible to recognize neuronal homologs
across species and perhaps determine how evolutionary
processes mold circuitry (e.g. Mizunami, 1994; Edwards, 1997; Tierney, 1995 for a discussion of the
effects of evolution on neural circuits). Moreover, information on neuronal homology can be applied by taxonomists to confirm or refute different evolutionary
scenarios (see Osorio and Bacon, 1994; Osorio et al.,
1995). The large number and diversity of insect species,
the known sexual dimorphisms of structure and behavior (see Hoy, 1990), the fact that insects lend themselves
particularly well to neuroanatomical study, and the fact
that the organization of insect ganglia bestows upon
their neurons distinctive and beautiful structures, all
contribute to making insects arguably the best animal
group with which to pursue such an endeavor.
Establishing neuronal homology across species is
notoriously difficult especially when the functions of the
neurons may be different. Normally it requires the
demonstration of a common lineage from an identified
neuroblast during development (e.g. Boyan and Ball,
1993, for segmental homologs). When neurons are particularly distinctive, structurally and functionally, however, it is possible to make the conclusion with
confidence (e.g. Bacon, 1980). In the orthopteran flight
system there is evidence in locusts and crickets for
similar organization of interneurons (Robertson et al.,
1982; Robertson, 1987a) and for homologous interneurons with roles in pattern generation (Robertson,
1987b; Hennig, 1990b). Nevertheless, possibly the best
system with which to investigate these sorts of questions is the visual system of the Diptera which can
provide answers even at the level of synaptic ultrastructure (Shaw and Meinertzhagen, 1986). Specific neurons
in the optomotor system are quite distinctive (e.g.
Hausen, 1982) and evolution of the visual system has
been relatively conservative. Moreover, numerous species of closely related Diptera occupy a variety of
different visual niches providing an opportunity to dissect differences related to adaptation from similarities
related to a common origin. Recent detailed neuranatomical studies in a large number of Diptera
demonstrate that whereas the retinotopically arranged
elementary motion detectors are conserved across different species (Buschbeck and Strausfeld, 1996) there
exist functionally and ecologically relevant differences
in the structure and organization of the output elements
of the lobula plate (the vertical and horizontal motion
sensitive neurons) (Buschbeck and Strausfeld, 1997).
The idea that conserved circuitry is tuned to produce
species-relevant behavior is confirmed in the control of
the leg during predator evasion of stick insects and
locusts (Büschges and Wolf, 1995). Typically stick insects become cataleptic on detection of a predator and
locusts generate jumps or kicks. The components and
operation of the motor circuits of the leg in these two
organisms are relatively well known, though certainly
far from completely described, and they exhibit much
similarity. Putative homologs exist at all levels of the
circuit (motor neurons, non-spiking interneurons, sensory neurons) and their interconnections are qualitatively the same. Interneurons, but not motor neurons,
show a difference in the velocity sensitivity of the
processing of information from the femoral chordotonal organ (velocity-dependent in stick insects but not
in locusts) and this can be attributed to the functional
demands imposed by different evasion strategies.
Thus, the characteristics of identified neurons in insects can be used to answer questions about evolutionary relationships among taxa and also can help in
parsing out functional vs. phylogenetic features. Central nervous circuits are relatively conserved, which is
not surprising given the difficulty of constructing the
nervous system during development and the phylogenetic conservation of the mechanisms for neurogenesis
and growth cone guidance (Goodman, 1994; Reichert
and Boyan, 1997). Fine-tuning of cellular and synaptic
properties overlies a common circuitry to fit an organism for an individual life-style, at a given developmental stage (Edwards, 1977), and in an individual
ecological niche.
4. Newer approaches
4.1. Molecular/genetic approaches
While the 1980s saw a burgeoning of the cellular
approach to understanding neural operating principles,
the 1990s have witnessed the flowering of various
molecular approaches; thanks to the increasing sophistication of techniques. It has been demonstrated that
identified neurons in the lobster stomatogastric system
have unique genetic identities (Baro et al., 1996) and
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
the same is without doubt true for insect neurons. The
organism par excellence with which to perform molecular and genetic manipulations is the fruit fly,
Drosophila. This insect has the usual repertoire of complex behaviors but its small size, and consequently the
small size of its neurons (diameter of motor neuron
somata 8 mm, Koenig and Ikeda, 1983), has prevented
sustained intracellular investigation of identified
circuitry.
One notable success was the investigation of connectivity from the descending giant fiber (GF) that mediates escape jumping and flight (see Wyman et al., 1984).
The effects of mutations that impaired escape could be
traced to defects in the GF circuit (see Thomas and
Wyman, 1983). Much molecular dissection of synaptic
operation is now performed using the larval neuromuscular junction (see Keshishian et al., 1996) and genetic
and behavioral experiments with Drosophila combined
with intracellular investigations in larger flies (e.g.
Musca) is advancing the genetic dissection of the neuronal basis of visuomotor flight control in flies (see
above). Now another central synapse, and one that is
involved in the control of flight, can be added to the
GF circuit as one accessible to modern techniques.
Intracellular recording from adult motor neurons has
allowed the demonstration that the shaking-B2 mutation disrupts synaptic connectivity between haltere afferents and flight motor neurons (Trimarchi and
Murphey, 1997; Fig. 11). It is to be hoped that contin-
429
ued efforts in this direction will lead to more complete
descriptions of functional circuits of identified neurons.
The particular advantages of Drosophila have meant
that efforts to use molecular and genetic techniques to
study behavioral circuits in adult animals continue unabated. One area where these approaches are just beginning to yield exciting results concerns the mushroom
bodies (and see Strausfeld, this volume). This area
contains an enormous population of Kenyon cells
which appear homogeneous by morphological criteria.
However, use of enhancer trap techniques has made it
possible to describe the developmental lineage of mushroom body cells (Ito et al., 1997) and provided the
realization that the adult mushroom bodies are composed of definable cellular subsets that differ in gene
expression (Yang et al., 1995). Such techniques may
spur studies of mushroom body involvement in sensory
integration, motor programming, and learning.
4.2. Neuroecology
In some respects to say that an organism’s ecology
constrains the nature of its neuronal circuits is simply
to restate the concept that evolution has matched the
design of the circuitry to the functional demands of a
particular ecology. It is this awareness that underpins
the discipline of neuroethology-control systems can
only properly be investigated in the context of the
natural behavior of the animal and this implies a con-
Fig. 11. The shaking B2 mutation disrupts synaptic connectivity between haltere afferents and flight motor neurons in Drosophila. (A)
Comparison in wild-type [Ai] and shaking-B2 [Aii] animals of the latencies of action potentials recorded intracellularly in motor neuron B1 after
stimulation of haltere afferents with increasing stimulus intensities. Note the irregular latencies and the unreliability of the connection in the
mutant. (B) Effects of electrical stimulation of haltere afferents on electromyographic activity recorded from muscle B1. [Bi] Stimulus at 70V,
comparison of five traces in each of a normal and a mutant animal. [Bii] Summary of latencies recorded with a variety of stimulus intensities. Note
the longer and variable latencies in the mutant animals. Taken with permission from Trimarchi and Murphey (1997).
430
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
Fig. 12. The visual ecology of voltage-gated K+ conductances in
insect photoreceptors recorded with single electrode voltage clamp. In
Diptera [top panel] non-inactivating outward currents are typically
found in diurnal, rapidly flying insects (fast) where as in crepuscular,
slow flying insects (slow) the outward s currents are found to inactivate thus minimizing the energy required to maintain normal K+
concentrations when the ecology dictates that high frequency responses are not necessary. In the locust Schistocerca the inactivation
properties of the outward currents are dependent on the time of day
[middle panel] and can be converted from the day state to the night
state by the application of 5HT [bottom panel]. Taken with permission from Weckström and Laughlin (1995).
sideration, if not a deep understanding, of the animal’s
ecology. Excellent recent demonstrations of the idea
that neural properties are matched to ecology concern
the visual system of flying insects. The distribution of
voltage-gated ion channels in photoreceptors of different insects (locust, fly, bee) has been shown to be
associated with the flight speed of the insect and the
time when it is most active (i.e. nocturnal, diurnal)
(Weckström and Laughlin, 1995; Fig. 12). Indeed,
potassium channels in locust photoreceptor membranes
are modulated on a daily basis to balance the goals of
increasing performance while active and decreasing
metabolic demands while inactive (Weckström and
Laughlin, 1995). Similarly, the tuning of visual motion
sensitive neurons in a variety of insects is correlated
with their flight speeds and hovering ability (O’Carroll
et al., 1996).
On the other hand it is increasingly likely that the
ecology of an organism could have longer term effects
that condition the circuits in such a way that their
operating mechanisms are different. For example, diet
has been shown to modify the ionic content of the
hemolymph and the activity of cockroaches (Pichon
and Boistel, 1963). Temperature is arguably the most
potent environmental variable for a poikilotherm, espe-
cially for the migratory locust whose native habitat
includes semiarid regions of equatorial Africa (Uvarov,
1966). In a flying locust thoracic temperature can exceed environmental temperature by around 10°C (WeisFogh, 1956, 1964) and in hot environments thoracic
temperatures of flying insects have been recorded as
high as 48°C (Coelho, 1991). Increasing thoracic temperature has effects on both the output of the locust
flight circuit (Foster and Robertson, 1992), on properties of identified neurons (Xu and Robertson, 1994) and
on synaptic parameters within the circuit (Robertson,
1993). These effects can be interpreted as mechanisms
to compensate for changes in temperature and thus
maintain a relatively stable output (Xu and Robertson,
1996). Interestingly, prior heat shock (exposure to 45°C
for 3 h) alters the thermosensitivity of flight behavior
(wingbeat frequency during tethered flight) and that of
the flight circuit (deafferented rhythm frequency) in
profound ways (Robertson et al., 1996; Fig. 13). The
thoracic temperature at which flight rhythm generation
fails is 6–7°C higher in heat-shocked animals and the
frequency of flight rhythms (intact or deafferented)
becomes insensitive to temperature in the 30–45°C
range (Robertson et al., 1996). Similar reductions of
thermosensitivity can be observed for the conduction
velocity and extracellularly recorded amplitude of the
stretch receptor action potential (Gray and Robertson,
Fig. 13. Prior heat shock affects the temperature sensitivity of flight
rhythms in the locust Locusta. [A] Wing beat frequency (WBF)
recorded electromyographically from intact tethered flying locusts
shows minimal thermosensitivity after heatshock (ii) compared with
control animals [i], and the temperature at which flight rhythms fail is
significantly higher after heat shock (iii). (B) Flight rhythm frequency
recorded from deafferented preparations is reduced in thermosensitivity at temperatures above 35°C after heat shock (ii) compared with
control preparations [i], and the temperature at which flight rhythms
fail is significantly higher after heatshock (iii). Modified from Robertson et al. (1996).
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
1997c) and for parameters of synaptic potentials in the
flight circuit (Dawson-Scully and Robertson, unpublished). The effects of heat shock on thermotolerance
last for at least one week (Robertson et al., 1996). It is
not yet clear how heat shock causes these effects, but a
working hypothesis is that heat shock proteins (expressed in locusts, Whyard et al., 1986) protect the
operation, and change the thermosensitivity, of membrane proteins. The significant feature of all of the
above is not just that environmental conditions alter
circuit properties, but also that the history of environmental conditions affects how the circuit operates at
any future time. This is neuroecology; the study of
features of neural structure and function that are constrained by the ecology of an organism.
Table 1
Web sites with information on identified nerve cellsa
Organism-based
1 Molluscs
2 Arthropods
3
4
5
6
In the space of this review it was only possible to
cover a small portion of the work on insect model
systems where identified nerve cells have been related to
the control of behavior. Furthermore, we did not consider the importance of using identified insect cells in
studies of: neuronal differentiation (e.g. Doe, 1992),
learning (e.g. Mauelshagen, 1995), or regeneration (e.g.
Spira et al., 1987), and we only touched briefly on the
development of synaptic connections (e.g. Blagburn
and Thompson, 1990). Thus it can be appreciated that
the total amount of published information on identified
nerve cells of insects is enormous.
The data management problem that a neuroscientist
faces when trying to marshal complete information
about one specific identified cell, to make a comparison
of potentially homologous cells across species, or to
obtain design features of different cells for modeling
studies, can be daunting. The same data management
situation arose in genomics at least a decade ago when
the number of published sequences was rising rapidly
(Smith, 1990). The solution for geneticists and molecular biologists was to rely on electronic databases (Aldhous, 1993). Searching a sequence database allows one
to quickly compare nucleotide or protein structures and
this can suggest functions for molecules where this was
previously unknown, or it may point to evolutionary
relationships.
The importance of storing neurobiological data in
digital formats has not escaped the attention of neurobiologists. The ability to tag molecular, developmental
and physiological information to the structure of
uniquely identified neurons, such as those reviewed in
this chapter, will make it possible for the same broad
questions to be answered for neural circuits, that are
now being asked for the genome of higher animals.
This point was persuasively argued in an article by
Rowell (1988) which seems to have been widely read,
but acted upon very little until the last three or four
years.
Fruitfly (3 mirror
sites)
Cockroach
Bee
Moth
Software/tools
7 Structural
8
4.3. Neuroinformatics and insect ner6e cells
431
Physiological
ganglion.med.cornell.edu/
web.neurobio.arizona.edu/Evolbrain/index.h
tml
flybrain.neurobio.arizona.edu/
flybrain.uni-freiburg.de/
flybrain.nibb.ac.jp/
n002bsel.bios.uic.edu/
beebrain.neurobio.arizona.edu/
mothbrain.neurobio.arizona.edu/
www.nervana.montana.edu/projects/NeuroS
ys/
soma.npa.uiuc.edu/isnpa/isnpa.html
a
All URL identification tags should be preceded by ‘‘http://’’.
Some sites (listed first) are concentrated primarily on collections of
data related to a given organism, while others (at bottom) are
devoted primarily to development and dissemination of software for
visualizing neuronal structures or physiological data
Electronic resources initially were implemented for
databases of images related to clinical and mammalian
neurobiology (e.g. Fox et al., 1993; Swanson, 1995).
The recent heavy usage of world wide web resources by
researchers has catalyzed the development of neurobiological tools, and several electronic databases related to
identified neurons in insects have now appeared. The
most extensive at this point is a collection of sites
devoted to the Drosophila CNS (Armstrong et al., 1995;
Heisenberg and Kaiser, 1995), but others are beginning
to emerge (see Table 1) and a federation of insect
databases has already been envisioned (see links at
Flybrain site). We believe that WWW resources will
prove to be particularly useful for precisely those areas
where insect neural studies have their brightest future:
comparative,
evolutionary,
and
computational
neuroscience.
5. Concluding remarks
There is no doubt in our minds that research using
identified neurons in insects is alive, well, and will make
significant contributions in a wide variety of sub-disciplines within the general enterprise of understanding
how neurons and their interactions generate appropriate behaviors. The growing realization of the computational power of single neurons and dendrites in
mammalian circuits (Koch, 1997; Sejnowski, 1997)
comes as no surprise to those who follow the literature
on identified neurons in insects (see references above on
Dipteran visual cells to provide examples from but a
single system) and in the Dipteran examples this computational ability can be directly related to the functional demands of the neurons.
432
C.M. Comer, R.M. Robertson / Progress in Neurobiology 63 (2001) 409–439
A theme which has emerged numerous times in this
chapter, with respect to both sensory processing and
motor programming, is the value of comparative study.
This is quite obvious in the case of evolutionary questions, but even with respect to computational questions,
comparisons of identified circuit elements in related
species, and especially between adults and larval forms
of the same species offer many opportunities.
Reviews like this chapter are likely to become obsolete as the mass of information becomes too unwieldy
to manage in a single chapter. Possibly the present
review will be taken as strong evidence by some readers
that we have already passed that point. This, in itself,
argues for the importance of readily accessible databases of identified neurons to future research that
would seek to define the interaction between evolutionary processes and identified circuits.
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