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547 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 548 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 549 ‘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 550 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. 552 Sensory systems 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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