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Olfactory processing: maps, time and codes
Gilles Laurent
Natural odors are complex, multidimensional stimuli. Yet,
they are learned and recognized by the brain with a great
deal of specificity and accuracy. This implies that central
olfactory circuits are optimized to encode these complex
chemical patterns and to store and recognize their neural
representations. What shape this optimization takes remains
somewhat mysterious. Recent results from studies focusing
on odor representation in the first olfactory relay (i.e. one
synapse downstream of the receptor neurons) suggest
a great deal of order and precision in the spatial and
temporal features of odor representation. Whether these
spatio-temporal features of neural activity are an essential
part of the code for odors (i.e. whether these features are
essential for the downstream decoding circuits) remains a
central issue.
receptor neurons expressing the same candidate receptor
molecule to single (or pairs of) glomeruli in the olfactory
bulb [3,5,6••,7]. Such projection patterns are bilaterally
symmetrical and, to the best of our knowledge, very
similar across conspecifics, suggesting precise ontogenetic
control. Given that the actual function of the putative
olfactory receptors (and the rules guiding their interactions
with ligands) still awaits demonstration, it is unfortunately
still not possible to assign a functional description to
these ‘receptor projection maps’. Assuming that these
candidate receptors are indeed olfactory receptors, we can
expect that the next few years will finally reveal the
functional logic for this complex glomerular neuropil and
help confirm or invalidate the functional hypotheses derived from already extensive physiological and anatomical
work [11–17].
Addresses
Division of Biology 139-74, California Institute of Technology,
Pasadena, California 91125, USA; e-mail: [email protected]
The tremendous wealth of molecular and histological data
allowed by the identification of a large, new multigene
family possibly encoding odorant receptors leads me to
consider here the apparent relationship between neural
maps and neural codes [2,18]. Although ordered maps
suggest precise developmental instructions and often
give the observer physical — or spatial — correlates of
function, it is useful to consider some of the fundamental
differences between brain maps and brain codes.
Current Opinion in Neurobiology 1997, 7:547–553
http://biomednet.com/elecref/0959438800700547
 Current Biology Ltd ISSN 0959-4388
Abbreviation
GABA
γ-aminobutyric acid
Introduction
The recent and beautiful descriptions of convergent
projections of olfactory receptor axons to the olfactory
bulb of vertebrates (see [1,2,3–5,6••,7]) unambiguously
indicate order and precise mapping in the olfactory system.
The interpretation of the role of these receptor projection
maps for olfactory coding, however, sometimes appears
inaccurate, prompting me to discuss here the differences
and similarities between maps (or circuit layout) and codes
in the brain, using examples from other modalities.
Recent physiological work on olfactory neurons in the antennal lobe of insects suggests that odors are represented
by both temporal and nontemporal aspects of the firing
of these neurons. I will discuss the evidence, its possible
significance for stimulus representation by the brain, and
the need for establishing a causal link between presumed
codes and perception.
Maps and codes: are they the same?
The past few years have yielded remarkable developments
in the molecular biology of olfactory receptor neurons
[1,2,8–10] and in the description of their spatial projection
patterns into the olfactory bulb [3–5,6••]. Most noticeable
are the reports of the exquisitely precise convergence
patterns of the axons of seemingly randomly distributed
A brain (sensory) map is a region of nervous tissue,
the physical arrangement of whose neurons is correlated
with spatial or functional features of the environment,
usually in an orderly fashion. Topographical maps are those
in which the physical arrangement of neurons respects
the neighborhood relationships of the input (e.g. x-y-z
coordinates in space, frequency of sound, etc.). Such maps
can be continuous (at a given scale) or fractured (as in the
cerebellum, for example), and usually assign more territory
to the areas representing inputs of greater behavioral
importance for the animal. The olfactory bulb is obviously
ordered [3,5,6••] and, therefore, probably maps something,
although what this something is (e.g. molecular epitopes,
size, etc.) still remains to be determined.
A code, by contrast, is a symbolic language — a set of
rules — by which information can be transmitted (e.g. if
neurons b, f, g and w reliably fire more than ten action
potentials during period P, then contract left knee flexor)
[19,20••]. To be useful, a code requires a decoder.
Unfortunately, what decoders and, therefore, codes the
brain uses remains unclear. Equating — or even relating
causally — the olfactory receptor projection map and an
olfactory code implies that the brain decodes the olfactory
bulb signals by using knowledge about the physical origin
of the inputs it has to decipher, just as a human observer
would do by looking at 2-deoxyglucose patterns, function
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Sensory systems
magnetic resonance imaging (fMRI) or activity-dependent
signals. Said differently, it implies that the computation
underlying the encoding or decoding of olfactory signals
by the brain makes obligatory use of neuronal position.
Although possible in principle, nothing really indicates
that this is the case. In fact, it seems to me that in no
sensory system, including the visual cortex (see below),
can we say unambiguously that mapping is an intrinsic and
necessary part of the code used by the brain. Mapping
is, without a doubt, relevant, useful and functionally
important but for reasons that are, I think, not necessarily
intrinsic to information coding per se.
bears no topological relation to the physical arrangement
of their place fields. Of all the brain regions, the one that
appears to represent behavioral space fails to reveal an
ordered map! Yet, as clearly shown experimentally, the
information about the animal’s position and trajectory can
be extracted by pooling and decoding (using rules that
have to do with total spike numbers and preferred tuning
of the neurons, but have nothing to do with the position
of these neurons in CA1) the collective activity of about
100 neurons [25]. If the brain can decode this information
using similar (but not necessarily identical) algorithms,
we must conclude that mapping of space in the brain is
irrelevant for the animal’s perception of its living space.
Where is a physical map an intrinsic part of the code?
A good example of a physical map being an intrinsic part of the code is mRNA. An mRNA molecule
comprises a linear sequence of nitrogenous bases, and
its physical arrangement encodes the amino-acid chain
whose ultimate folding produces a functional protein.
The physical sequence encodes the protein, however,
only because the decoder (i.e. the ribosome) reads the
mRNA sequentially and linearly. One could imagine a
different translation process that would not use such a
rule — for example, one that would read out bases that
are not neighbors in the mRNA, but rather ones that bear
specific sequential tags, independent of the position they
occupy in the chain. Nature probably opted for a ‘simpler’
solution — the one we know — given the constraints of
cellular microphysiology. In this case, we can say that
a cell truly uses the physical, linear map of an mRNA
molecule to encode a protein. This one-dimensional map
is, therefore, an intrinsic part of the code.
Where is a physical brain map unlikely to be part of the
code?
I can think of two simple examples of physical brain
maps not being obvious parts of the code: one in the
visual cortex and the other in area CA1. In the visual
cortex of primates, beautiful topographical maps of the
retinal surface (and thus of the visual world) have been
clearly established [21,22]. Although these maps clearly
exist, they generally exist on a large scale. Indeed, if one
considers an area smaller than that of a hypercolumn,
topography often disappears [21,22], probably because, at
this scale, the cortex trades positional information for other
attributes, such as orientation. Yet, this area represents a
foveal visual angle much greater than the visual resolution
limit. Thus, position in the topographic map does not carry
sufficient information to explain accurate perception of
fine visual details. The second example is taken from a
‘higher’ cortical area, the CA1 region of rat hippocampus.
There, pyramidal cells appear to represent the living space
of the animal, such that most pyramidal neurons can be
described by one or several ‘place fields’ [23,24]. (In the
rat, the place field of neuron x is the region of space in
which the rat needs to be, or move through, to activate
neuron x.) This system is remarkable because the physical
arrangement of place cells (the CA1 pyramidal neurons)
Where might a brain map be intrinsically linked to the
code?
An interesting example of where a brain map may be
intrinsically linked to the code is the highly specialized
sound localization system of barn owls [26,27]. Jeffress
[28] proposed and Konishi and Carr [29,30] showed that
sound localization in the horizontal plane relies on the
sensitivity of the owl’s auditory system to phase delays
between the signals arriving at each ear. This sensitivity
relies on the tuning of neurons in nucleus laminaris, a
brainstem nucleus, to microsecond range coincidence of
inputs from the left and right sides. These neurons lie in
a multilayered sheet that maps sound frequencies in the
plane of the sheet, and left–right time differences across
the sheet. The reason why mapping of delays and coding
are very tightly linked here is that the decoding makes use
of physical delay lines of variable lengths (the axons of the
input fibers) to compensate for the sound delays between
the left and right ears, thus producing input coincidence
at particular depths within the sheet. In nucleus laminaris,
the depth of a neuron more or less determines the lengths
of input fibers necessary to reach it and, therefore, the
left–right time delay to which it is best tuned. The map
along the depth axis is, therefore, intrinsically linked to the
code. A similar encoding occurs in the auditory system of
mammals [31]. However, the use of physical delay lines
does not, on its own, necessitate the creation, or use, of
an ordered map of time delays. Axons could meander to
increase conduction times or increase their diameter to
conduct faster, and hence they could, in principle, project
to a given position via many different lengths of cable. A
neuron that receives afferent inputs of a given length could
therefore be placed in many possible positions, without
imposing any fundamental change to the code or to the
decoding. As we will see below, the ordered map of delays
is probably the best physical compromise that serves both
the decoding and the practical need for parsimony. These
examples hopefully illustrate both the difficulty in relating
maps and codes and the care with which both concepts
should be handled.
Why do brain maps exist?
Why then do we find ordered maps in most sensory
systems? One conservative hypothesis is that ordered
Olfactory processing Laurent
maps are the best response to severe developmental and
physical constraints. Let us suppose that the convergence
of similarly tuned olfactory receptors is an optimal way
to increase signal-to-noise ratios by averaging out uncorrelated noise. Why would such a functional constraint be
best served by converging projections to single glomeruli?
After all, each postsynaptic neuron that does the averaging
could simply extend long and possibly electrically active
dendrites [32] in every direction to contact the appropriate
afferents wherever they lie. This would accomplish the
same computation (the convergence would be done by
the postsynaptic neuron’s dendrites rather than by the
afferents’ axons), but it would be wasteful. Inordinate
amounts of dendrites would be added (making brains and
heads larger), and the rules of CNS development would
probably need to be more complex than they already
appear (‘find all functionally related afferents’, rather than
‘go to x-y and contact whatever is there’). A similar
reasoning can be applied to the computational need for
lateral inhibition, which is useful to enhance contrasts [33]
and is therefore used best when applied between closely
related inputs (in space or any other useful dimension). It
makes sense, for developmental and ‘cabling’ reasons, to
pack related inputs near each other so as to facilitate such
lateral interactions.
Isomorphic maps (i.e. ones that respect the topology of the
space they represent, as in a retinotopic map) are also very
useful designs because they allow the maps constructed
via separate modalities (vision and hearing, for instance) to
be put in physical register extremely easily, as found in the
superior colliculus [34]. Here also, packing, convergence,
and topographical organization are not essential and can be
seen simply as a means to optimize circuit layout, rather
than stimulus encoding. Modelling studies of visual cortex
development indicate that maps of ocular dominance
and orientation preference can be obtained by using a
simple Hebbian rule (which bears no obvious relation
to the coding principles underlying vision, but rather
imposes that ‘connections between neurons that are active
together will be reinforced’) for the establishment of
synaptic connections [35]. Similarly, experiments adding
a third eye to a frog’s head lead directly to the creation
of ocular dominance columns in its tectum, something
that never occurs naturally in this animal [36]. These
results, together, also suggest that topographical mapping
may owe more to the optimization of circuit layout and
developmental rules than to neural coding per se.
In summary, it is important to distinguish carefully the
concepts of maps and codes, and to use them interchangeably only when it can be shown that decoding — by
the brain, not by a human observer — uses positional
information. What is probably most important (though not
sufficient) for coding in the brain is the neural connectivity
matrix, rather than the position of each neuron. Neurons
are not like gas molecules that can interact only with their
neighbors and for which, therefore, position determines
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‘connectivity’. Neurons can extend long processes and
thereby contact, in principle, any other neuron in the
same brain. The position of each neuron may, therefore,
be determined by constraints that owe more to design
and ‘manufacture’ optimization [37] — just as for computer
chips — than to coding principles. This said, it needs to
be emphasized that knowing that (and how) a system is
mapped provides invaluable information that can only help
in deciphering the neural codes it uses. For this reason, the
demonstration of the precise projection patterns between
the nasal epithelium and olfactory bulb is a fundamentally
important accomplishment that will without doubt help
unlock the mysteries of olfactory coding.
The possible importance of time
Most of our sensory experiences are dynamic. We listen
to speech and music, observe insects (some of us), cars
and children, and, therefore, are constantly assessing the
state of our changing sensory environment. Our ability to
deal with such complex situations — such as our ability
to understand the sequences of sounds in a spoken
sentence — proves that the brain contains devices that
recognize or decode precise temporal sequences of neural
events. Justifiably less familiar, though not new, is the
idea that temporal sequences of neural events might be
used also to encode and decode stimuli that are, to a
degree, static, such as a short odor puff. Recent work
on olfactory processing in insects from my laboratory
[38,39••–41••,42,43] suggests that information about odor
identity can indeed be obtained by considering not only
the ‘spatial’ component of the response of ensembles of
neurons (i.e. which neurons are active — ‘which’ rather
than ‘where’ they are), but also the precise timing of
their activity. This suggests that the encoding of complex
natural stimuli such as odors may involve a temporal
element, and raises some issues regarding the ability of
current physiological and functional investigative methods
to reveal such fine features of neuronal activity in this and
other systems.
When an airborne odor is presented to the antenna of
a locust, a population of projection neurons is activated
in its antennal lobe [38] — the analog of the vertebrate
olfactory bulb [16]. The number of activated neurons is
estimated to be ∼10–15% of the total number of available
neurons (i.e. 80–100 out of 830 [38,43]), and this number
does not appear to depend on whether the odor is a single
molecular compound or a complex blend (such as a flower
fragrance) [38]. Specificity appears to be achieved, in part,
by the combinatorics of the representation: many neurons
participate in encoding many odors (the ensembles thus
often overlap), but each odor is represented by a specific
ensemble — a view consistent with imaging data from
other animals [11,13]. A locust’s ability to distinguish
several odors thus probably relies on the ability of neural
networks downstream of the antennal lobe to ‘separate’
the assemblies that represent these odors. Temporal
features may play a role in this process. Indeed, the
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Sensory systems
neurons responding to a given odor elicit a variety of
temporal firing patterns throughout the odor delivery
[38,39••]. Such patterns are reliable for each neuron–odor
combination, but differ across neurons for one odor and
across odors for one neuron [38,39••]. The complexity of
the temporal response patterns bears no relation to the
complexity of the odor [38]. The ensemble response to a
single odor presentation is therefore dynamic and is carried
by an ‘evolving’ assembly of firing neurons [42]. Temporal
firing patterns have been seen also in the projection cells
of the antennal lobe, or olfactory bulb, of many other
animals [13–15].
The temporal patterns displayed by odor-activated neurons in locusts have several additional attributes. Most
projection neurons responding to an odor show clear subthreshold membrane potential oscillations at a frequency
of 20–30 Hz [38]. These oscillations are synchronous
with 0 mean phase-lag in all responding and oscillating
projection neurons [38]. Hence, projection neuron spikes
produced during an odor response occur periodically, and
the coherent firing of the many odor-activated projection
neurons causes 20–30 Hz local field potential oscillations
in one of their target areas, the mushroom body [38,42].
Such oscillatory extracellular field potentials have also
been observed in the mammalian olfactory bulb [44], as
well as in molluscs [45••] and amphibians [46].
In the locust, information about odor identity [38,39••] or
concentration (G Laurent, unpublished observation) does
not appear to be contained in the phase of the projection
neuron spikes relative to the population average, as has
been proposed by several theorists for this and other
sensory systems [47,48]. More subtle is the finding that not
all the spikes produced by a projection neuron during an
odor response phase-lock to the field potential (or to other
projection neurons) [39••]. Rather, only spikes that occur
in certain precise temporal windows (where these windows
are depends both on the neuron and on the odor) during
the response phase-lock to the field oscillation. The other
spikes occur randomly (i.e. at inconsistent phases relative
to the field over repeated presentations). As a result, two
projection neurons that respond to the same odor over
the same period (e.g. 800 ms) will often phase-lock to one
another during only a few cycles (but always the same ones
for the same stimulus) of their response. It is therefore
often difficult to detect such transient synchronization
between neurons, especially if one uses cross-correlation
techniques that average over time periods much longer
than the probable duration of pairwise synchronization.
For this reason, it is crucial to carry out cross-correlation
analysis piece-wise over short time windows.
Whereas phase does not appear to be important, spike
timing relative to the order of the cycles of the odorevoked field oscillation does contain information about
the stimulus [40••]. Indeed it is often possible, for each
neuron, to measure a cycle-specific probability of spiking
in every cycle of the oscillatory population response
evoked by a given odor. In addition, two different odors
can evoke nearly identical responses (in the traditional
sense) in a given projection neuron (i.e. the same
average number of spikes and identical peristimulus time
histograms — provided the bin size is greater than the
oscillation cycle duration) but different cycle-by-cycle
spike probability assignments. When the rank order of
each spike is considered, for example, the seemingly
identical responses of a neuron to two odors suddenly
differ [40••]. For example, one odor could cause a
projection neuron to fire preferentially in cycles 1–3,
whereas another odor could cause it to fire the same
average number of spikes, but in cycles 2–4. In other
words, information about the odor is contained in the
arrival time of each spike relative to the population
oscillation. Hence, the oscillation could be thought of
as a clock signal, and each cycle of this clock can be
characterized by an assembly of synchronized neurons.
Each odor, therefore, evokes activity in a succession
of synchronized assemblies, and the field oscillation,
meaningless on its own, tells us how frequently this
representation is updated. If the transient synchronization
of two neurons is very difficult to observe when most of
the spikes they produce are not synchronized (see above),
this ordering of spikes is even more difficult to observe
because an experimenter does not always have the right
time reference. In our experiments, the stimulus was set
up such that the onset of the oscillatory response was very
precisely time-locked to the odor pulse onset [40••]. It
was thus possible to assign a precise rank order to each
projection neuron spike without arbitrarily shuffling any
of the physiological recordings obtained over many trials.
In a natural or lab situation in which such locking to the
stimulus onset does not occur, however, it is extremely
difficult to assign a rank order to a spike unless one has a
reliable record of the putative internal clock signal (e.g. the
field oscillation).
What might such a temporal representation be good
for? An argument often advanced against a temporal
scheme is that it is unnecessarily ‘complicated’ and
that there is “enough room” for time-independent representations of odors. Indeed, if in this system, each
odor is represented by any ∼100 neurons out of 830,
an astronomical [830!/(730! × 100!)] number of possible
combinations and, therefore, stimuli can, in principle, be
encoded. One problem with this argument, however, is
that the responses of neurons are probabilistic, whereas
the animal often does not have the luxury to average
over many stimulus presentations. Noise can therefore not
always be averaged out by repeated samples. In addition,
we do not know how many of these neurons are used
by the downstream decoding circuits. Furthermore, many
behaviorally important stimuli in the natural world are
similar but not identical (say ripe versus unripe, your pup
versus her pup or, to use a different modality, this face
versus that face). The separation of their representations
Olfactory processing Laurent
may, therefore, need as many cues as are available at once
(i.e. often over the duration of a single sampling), and
temporal features might offer such segmentation cues.
Imagine the representations a and a′ of two odors A and
A′ that share 90% of their neurons. Over a single average
sampling with either odor, maybe 70% of the neurons
known, on average, to fire in response to these odors
will actually fire when they should. The overlap between
these two single-sample representations (a and a′) will thus
probably increase, making their dissociation more difficult.
Suppose now that the neurons in a and a′ each respond
with temporal patterns and that these patterns change,
even in the subtle ways that we demonstrated [40••],
when A′ is substituted for A. What such temporal features
might allow is a multitude of independent neural samples
(the ensembles firing at each cycle of the oscillation) and
the ability to assign meaning to the precise succession of
‘neural sequences’ (e.g. the sequential ensembles firing in
cycles 1–3, 2–4, 3–5, etc.) during a single odor exposure.
In other words, the stimulus would be represented by
a constellation of different representations (some static,
others dynamic, and all, admittedly, noisy) that would each
act as a separate cue for recognition. The brain would
thus use many representations for the same object. A
prediction, therefore, is that such encoding and decoding
mechanisms might be more important for the separation
of similar stimuli because they are likely to have similar
‘spatial’ (i.e. time-averaged) representations. How could
this be tested?
While we have demonstrated that information about odor
stimuli is indeed contained both in the identity of the
activated neurons (what I call the ‘spatial’ or time-averaged
aspect) and in the temporal features of their activation, we
are still short of our goal, which is to find whether temporal
information is actually used by the animal [38,39••,40••].
This requires a behavioral or psychophysical assay. One
clue would be provided by showing that neurons can,
in principle, be sensitive to the temporal order of their
inputs. While we do not know that this is the case in the
locust olfactory system, we know that it is true (even if we
do not understand how) in certain songbird song-specific
neurons [49]. This finding proves that slow temporal
sequence sensitivity is at least possible in neurons. A
second approach would be to manipulate specifically the
temporal activity patterns and to test their importance for
odor learning and recognition. While this experiment is not
yet possible, an approximation of it is.
Synchronization of projection neurons in response to
odors results primarily from the actions of GABAergic
local neurons in the antennal lobe [41••]. Surprisingly,
blocking fast GABA receptors in the antennal lobe
selectively desynchronizes the projection neurons, but
modifies neither their responsiveness to odors nor their
slow temporal response profiles [41••]. This result is
551
interesting for several reasons. First, it provides direct
experimental support to the models of Rall et al. [50] and
Freeman [51] on the genesis of olfactory bulb oscillatory
synchronization, especially because granule cells inhibit
mitral cells directly, even, as in locusts [41••], in the
absence of Na+ spike-mediated synaptic transmission
[52]. Second, and most importantly, it indicates that
it is possible to selectively desynchronize ensembles
of neurons in vivo, without otherwise altering their
response profiles. This opens the way to direct behavioral
tests in which odor discrimination by intact animals
is compared with that in ones where synchronization
has been abolished. Such experiments should tell us if
temporal aspects of firing contribute to odor encoding and
decoding.
A final related and interesting note concerns recent
attempts to develop synthetic odor sensors that are at the
same time ‘broad-band’ (i.e. not too specific), adaptive
and sensitive. Such engineering-neuromorphic approaches
have been applied successfully to other sensory systems in
the past [53], and could, in addition to being very useful
in a minefield or in one’s kitchen, provide a real-world
testbed for one’s favorite coding hypothesis.
Dickinson et al. [54••,55] have recently built an artificial
nose consisting of an array of 19 multi-analyte optic fibers.
The fibers’ tips are coated with a fluorescent dye that is
immobilized in a different polymer at the end of each
fiber. Each polymer matrix has a different combination of
polarity, hydrophobicity, pore size, flexibility and swelling
tendency, which confers to each fiber’s tip a unique
sensitive range to organic vapors. Because the fluorescence
spectral changes of the dye depend on the polymer in
which it is embedded, the ensemble of fibers provides
a unique optical signature to a given odor that can be
decoded using simple feedforward neural networks. What
is interesting is that the performance of these artificial
neural networks (although they bear no resemblance to
animal olfactory circuits) improved markedly when the
temporal fluorescence profiles of the fibers (which again
bear only weak resemblance to neural temporal response
patterns) were used in training and testing of the networks
[54••]. This finding supports the idea that the timing
of signals in the olfactory system may contain essential
information for the encoding and decoding of olfactory
(and possibly other) stimuli.
Conclusions
These are very exciting times to be working on problems
related to olfactory processing. The demonstration of clear
physical organisational principles by molecular biologists
and the work of physiologists suggesting distributed and
temporal codes in the CNS will hopefully soon lead to a
fundamental understanding of the principles underlying
olfactory perception. The years to come will, without a
doubt, continue to be very stimulating.
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Acknowledgements
The work in the author’s lab was supported by the National Science
Foundation and the Sloan Center for Theoretical Neuroscience at Caltech.
Many thanks go to my collaborators Katrina MacLeod, Michael Wehr and
Mark Stopfer, and to Erin Schuman, Mark Konishi, Kenneth Miller, and
David Anderson for discussions and much constructive criticism.
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