Contributionsof topographyandparallel processingto odor codingin the vertebrateolfactorypathway John S. Kauer Odor information appears to be encoded by activity distributed across many neurons at each level in the olfactory pathway. Thus olfactory circuits function as parallel distributed processors. New methods for observing distributed activity in such systems permit computer simulations to be constructed that are constrained by patterns of activity observed in the real system. Analysis of the system using a combination of physiological measurements and computational approaches might elucidate the principles by which odors are discriminated. We still cannot clearly describe the mechanisms by which odor stimuli are encoded by the vertebrate olfactory system. One impediment to our understanding has been the difficulty of defining, in detail, the primary process of odorant transduction, although major progress has recently been made in biochemical and patch-clamp experiments that provide evidence for an odor receptor/G protein/adenylate cyclase cascade in the cilia of olfactory receptor cells. These processes appear to underlie transduction for a number of odorants, but several other mechanisms might also function, including inositol phospholipid turnover and direct ligand-gated channels. (For a review of these various transduction mechanisms see Ref. 1.) The identification of these putative transduction processes has brought into finer focus a long-

recognized difficulty in understanding how odor informarion is handled after transduction has taken place. That is, how do distributed, relatively nonspecific, single-cell responses observed at receptor, bulbar and cortical levels encode highly specific molecular properties of odor stimuli that are clearly discriminated by animals and humans at the perceptual level? Distributed odor responses The distributed character of vertebrate olfactory responses is observed within individual cells, where single-unit recordings show that any given receptor cell is commonly sensitive to many compounds, some of which might smell quite differently to humans 2. These data indicate that there are several different receptor sites on each receptor cell and raise the question of how a single, downstream, second messenger system such as a G protein/adenylate cyclase or inositol 1, 4, 5-trisphosphate (IP3) process might give rise to the varied temporal patterns seen in the spike responses. Additional evidence for distributed processing across the receptor cell population is seen in studies showing that responses to any one odorant are found in many different cells over wide areas of the olfactory mucosa3. These studies indicate that many dispersed receptor cells participate in the encoding of any one odorant. This distributed responsivity is also seen at the level of the olfactory bulb. Multi-unit and EEG

JohnS.Kauerisat the Departmentsof Neurosurgery, AnatomyandCell Biology,and Neuroscience,Tufts/ New EnglandMedical Center, 750 WashingtonSt, Boston,/VIA02111, USA.

~ o ~.

BULB

~o

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Schematic diagram of the elements making up the peripheral olfactory pathway from receptor epithefium to the output of the olfactory bulb. The inset shows the shapes of the intact nasal cavity and olfactory bulb in situ. The larger drawing shows the nasal cavity opened on its lateral margin and an exploded view of the bulbar layers. The general path of air flow during inhalation and exhalation is shown by the large transparent arrow. Abbreviations: epl, external plexiform layer; glom, glomerular layer; G., granule cell; grl, granule cell layer; M., m/t cell; mot, medial olfactory tract; m/t, mitral/tufted cell layer; on, olfactory nerve; P., pg cell. Fig. 1.

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79

recording 4'9, 2-deoxyglucose (2-DG) mapping 6'7 and voltage-sensitive dye recording 8 have shown that response patterns to an odorant are widespread and overlap with those elicited by other stimuli. Similar observations have been made in the piriform cortex 9, although this structure needs to be studied further before conclusions can be made. Thus, unlike insect olfactory systems, there are few data that indicate the presence of highly selective, 'labeled' lines at any level of the vertebrate main olfactory pathway, although an argument has been made for labeledline type routes in accessory olfactory structures during development 1°. Odor recognition seems to be carried out by responses encoding the molecular properties of the stimulus being distributed across many neurons activated in parallel: first in the populations of receptor cells in the nasal cavity; then in the populations of cells in the olfactory bulb, and so on up the pathway. Of course, the form in which an odorant is represented and the information about it that is extracted need not be the same at each level of the pathway. Other evidence for the distributed character of these responses comes from lesion experiments, in which large portions of the olfactory bulb can be removed with little deterioration in odor detection. Indeed, even regions of the bulb that have been identified by 2-DG mapping as having activity related to particular odors can be removed with minimal effect on behavioral responses to these odors n. This indicates that a substantial ability to identify the stimulus is retained even after damage to the integrative neural machinery of the bulb, and that information encoding odor identity is distributed among the remaining cells. Similar partial-lesion experiments have not yet been carried out at the level of the olfactory epithelium. The olfactory system also displays other properties that relate to distributed information processing. It ODOR

EPITH

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I~

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was shown some years ago that olfactory receptor cells undergo cycles of degeneration and regeneration with half-lives of about 30-50 days 12, so that the conveyance of odor information from the receptor cells to the bulb does not occur over stable pathways; that is, substantial turnover takes place during the time that critical odor perceptions, such as food finding, mate recognition and territorial marking, continue to be made. Additional experiments indicate that there are also other forms of plasticity in the system; these show that the significance of an odorant can be manipulated by odorant exposure. For example, odor cues that mediate suckling behavior in rat pups can be changed by in utero exposure to novel odors that can then serve as new cues for the behavior 13. There is, therefore, sufficient plasticity at this early stage to allow the complex, life-sustaining behavior of suckling to be triggered by a new input response pattern elicited by an extrinsic odorant. Recently a related phenomenon has been observed in adult, male humans. Approximately halt of the normal adult population is unable to smell the odor androstenone, a steroid in sweat that usually has an unpleasant quality for those who can perceive it. Wysocki et al. ~4 found that they could induce the ability to smell androstenone by systematic and repeated exposure to the compound in subjects who were previously unable to smell it. This suggested that there might be a latent population of receptors sensitive to androstenone, even in the anosmic individuals, and that odor exposure in some way could increase the number of these receptors available to the point where sensitivity to the substance was increased beyond perceptual threshold. These flexible, distributed characteristics of odor processing, coupled with our incomplete knowledge about which chemical properties of stimuli are encoded by the system, have made it difficult to relate single-unit activity to odorant quality - unlike the EPL

M/T

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Fig. 2. Schematic diagram of the circuitry of the epithelium and bulb. Highly interconnected relationships occur between

the epithelium and glomeruli, within the glomeruli via pg cells, among the m/t cells via granule cells and via recurrent collaterals from m/t axons. Reciprocal connections with olfactory cortices (for example, anterior olfactory nucleus and piriform cortex) form another level of interconnections (see Ref. 23). Cell bodies in the epithelium, glomeruli, M / T and granule layers are shown by black circles. Thinnest lines are axons; thickest lines are dendrites. All granule cells are reciprocally connected to secondary dendrites of m/t cells in the EPL as is shown for two of the granule cells. Abbreviations: EPITH, olfactory receptor epithelium; EPL, external plexiform layer; GLO/VI, glomerular layer; GRL, granule cell layer; MOT, medial olfactory tract; /Vl/T, mitral/tufted cell layer; ON, olfactory nerve layer; pg, periglomerular cell; rc, recurrent collaterals. 80

TINS, VoL 14, No. 2, 1991

situation in the visual and auditory systems where spike trains correlate well with stimuli that are physically and chemically well described. One problem might be that in a distributed processor like the olfactory system the 'grain' of single-unit observations is 'too fine' to make similar correlations, although such recordings have helped to reveal how the synaptic organization of the bulb relates to temporal aspects of the responses 1~-18. Additional difficulties in understanding stimulus-response relationships have come from the lack of a clear understanding of the topographical relationships among the levels of the system. If these studies reflect real properties of the olfactory system and not just deficiencies in design and execution of our experiments, we are then confronted with the need to develop new investigative and conceptual tools to deal with information processed by distributed, parallel events. Although new approaches are especially important for understanding the physiology of olfaction, since this mode of handling information seems prevalent at all levels of the system, novel approaches might also be relevant to the study of many other brain regions that almost certainly also function in parallel modes. To analyse parallel information processing in the olfactory system, two problems seem to require particular attention. The first is the need for a finely detailed description of the topographical and functional relationships between and within each processing level, accompanied by descriptions of the spatial and temporal distribution of odor-triggered activity. Methods that allow both high spatial and temporal resolution of responses might be a way of surmounting the 'grain' problem mentioned above 19. The second is the need for the development of formal, mathematical methods that can be used to characterize parallel neuronal responses. These then must be tested for their ability to represent events in the real system accurately. Recent progress on computations performed by simulated neural networks might particularly increase our physiological understanding ~°'21.

Odor processing in the olfactory epithelium and bulb The following description summarizes the organization of the olfactory system and information about odor processing in order to underscore some of the parallel events occurring in the olfactory epithelium and bulb. It is intended to provide background on olfactory function for comparison with ideas emerging from the analysis of synthetic neural networks (see also Gelperin et al. 'e2 on the olfactory lobe of the snail Limax, Haberly and Bower on the piriform cortex 23, and Sclfild24 on the olfactory bulb). This discussion focuses on the work done on the salamander Ambystoma tigrinum, a species is that has served as an experimental animal from which anatomical, physiological and behavioral 25 information can be gathered. Figure 1 schematically summarizes some of the major structural and functional features of the olfactory receptor epithelium and bulb. The receptor epithelium in the salamander is an elongated, flattened sac lined with olfactory receptor cells on both dorsal and ventral surfaces. This rather simple configuration contrasts with the complicated turbinate folds in the mammalian nasal cavity, but provides the opportunity TIN& Vol. 14, No. 2, 1991

for carrying out physiological experiments with strict control over the stimulus. The mucosa is fairly uniformly composed of bipolar receptor cells embedded in a matrix of supporting cells. The ciliated ends of the receptor dendrites lie on the surface of the mucosa and the receptor cell axons, bundled together to form the olfactory nerves, extend into the cranial cavity to terminate in the glomerular layer of the bulb. In Fig. 1 the broad responsivity of single receptor cells to different odors is schematically depicted by showing cilia of different colors on each cell, representing the fact that each cell is sensitive to a variety of odorant molecules (for example, sensitivity to odorant A is shown by red, to odorant B by green, and to odorant C by blue). The observation that responses to a single odorant (one of the colors) are distributed across the olfactory epithelium is shown by the presence of responsive cells (those having at least one cilium of that color) throughout the mucosa, with higher densities or higher sensitivities of responsive cells (greater numbers of cilia of one color) to a particular odorant found clustered in certain regions. For example, more cells responsive to odorant A (that is, more red cilia) are seen in the anterior part of the cavity, but there are also cells responsive to odorant A outside the clusters, although they are less responsive (that is, fewer cilia). This nonhomogeneous pattern of sensitivity has been described in a number of studies in which the electro-olfactogram (EOG) was used to map the distribution of activity to a variety of different odorants (see, for example, Ref. 3). It should be emphasized that this description does not make any assumptions about how receptor cells are able to respond to a variety of different compounds. That is, the responsivity of a single receptor cell to several odorants could arise because of the presence of several different receptor sites with which different recognition sites on different odorants interact, or because of the presence of one identical receptor site that interacts with the same recognition site occurring on several different odorants. Of course both possibilities could also hold. The topography of the connections between the epithelium and bulb is complex and is still not fully defined26, but one essential feature appears to be the lack of a precise point-to-point mapping of the kind described in visual or somatosensory systems (see Figs 1, 2). That is, each region of the epithelium sends some axons to relatively restricted bulbar regions as well as sending other axons to widely distributed termination sites in the glomernlar layer. The degree of this dispersion may vary in different species. For example, in the salamander it appears that the axons from a local epithelial region can extend to much or all of the glomernlar layer27'2s, while the distributed projections might be restricted to somewhat less than the total glomerular surface in rabbits 29'3°. The corollary of these observations is that localized glomerular regions receive (to varying degrees) input from large areas of the receptor surface, thereby providing a subtly elegant pattern of interconnectedness that could form the basis for a distributed network architecture and that could, in itself, serve as a mechanism by which epithelial odor responses might be dissected for bulbar integration. Similar patterns of connections have been reported in the rat by Astic et al.31. This pattern of combined 81

A a

B

C amyl

acetate

a

limonene

b

ethyl-n-butyrate C

Fig. 3. (A) Unfolded display of 2-DG uptake in the glomerular layers of the left (L) and right (R) olfactory bulbs of a salamander after stimulation with pure air (left panel) and with amyl acetate (right panel) at 1/100 vapor saturation. The blue, midline region is the area between the bulbs outside the tissue, green indicates the level of background 2-DG uptake in the tissue, red, black, and purple are levels of increased 2-DG uptake (each level = one SD). (B) Sequential 33 ms frames from a voltage-sensitive dye recording of responses elicited across the olfactory bulb after electrical stimulation of the Olfactory nerve. (Taken, with permission, from Ref. 54.) This view is looking down onto the top of the planar, bulbar layers from a perspective similar to that shown schematically in Fig. 2. Green, depolarization one SDabove background; red, two sDs. Abbreviation: mot, medial olfactory tract. (C) The first six consecutive frames from voltagesensitive dye recordings of bulbar responses to three different odorants (a, b, c) (viewing perspective is the same as in B). Odorants were defivered at 0.06 of vapor saturation (about 6 x 10-6 M). Notice different onset latencies, different magnitudes and different activation patterns in frames that show the first response to the three odors. Colors from blue to red indicate depolarization covering one to two SDSabove background. 82

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convergence and divergence might underlie the dispersion of information processing observed in the lesion studies already mentioned, by conferring redundancy on input to local regions of the bulb26'31. This divergent/convergent pattern is the first of several that occur in the olfactory pathway: in the epithelium to bulb connection; in lateral interactions within at least two levels of the bulb; and in the pirlform cortex32. A detailed understanding of the specifics of connectivity within these patterns is important for accurately designing neural network computer simulations that may be useful for gaining insight into real integrative processing. Given these properties, how might the encoding of odors at the level of the receptor epithelium take place? A mixture of air and odor is drawn into the nasal cavity by inhalation or by an intentional sniff. Odorant molecules are distributed to different regions of the epithelial receptor sheet by several mechanisms including turbulence in the air flow, the shape of the nasal cavity (this may have a profound effect in the complicated nasal cavities of mammals), and by chromatographic-like interactions of the compound with the mucosal surface33. By these mechanisms, odorant molecules with different chemical properties arrive at various areas of the mucosa as different, nonuniform patterns before they interact with any neuronal part of the system34. At each of the various regions of the epithelium, the odorant molecules diffuse into the mucus covering the receptor cell cilia, probably bind to odor-binding protein(s) (either before, during, or after contacting the receptor cell membranes35) where the transducing event occurs by, for example, the receptor/G protein/adenylate cyclase cascade or other mechanisms already mentioned. The receptor cell membranes are depolarized, eliciting bursts of action potentials (onsets and durations of which vary for different odors36) that propagate down the slow, C fiber, olfactory nerve axons that terminate in the glomeruli of the bulb. Since the odorant interacts with a large number of distributed receptor cells, each having ffffferent degrees of responsivity, the molecular properties of the stimulus are now encoded by an array of action potential temporal patterns travelling (perhaps at different rates in different sized axons) e n m a s s e to the bulb. Precisely how the molecular characteristics of the stimulus are represented in this array of activity is unknown. Taking the properties of connectivity between the epithelium and bulb described above into account, it seems likely that spatio-temporal patterns formed at the epithelial level are rearranged during transmission to the giomeruli where they mightthen be integrated by synapfic circuits in the bulb. The basic circuitry of the olfactory bulb has been described many times (see, for example, Ref. 37) and its essential features appear to be similar in many different animals. A highly simplified view of some of its major elements is shown in Figs 1 and 2. In the salamander, the layers of the bulb are arranged in planes rather than in the concentric laminae more commonly seen in the mammal. In the glomernli, afferent olfactory nerves form excitatory synapses on the principal output cells of the bulb, the mitral/tufted (n~t) cells, as well as on interneurons, the periglomerular (pg) cells. The pg cells have pairs of reciprocal excitatory/inhibitory connections with the TINS, VoL 14, No. 2, 1991

A

Q.

hidden units

Fig. 4. Similarities of the olfactory bulb to neural networks. (A) Generalized diagram of neural network architecture (from Ref. 47). (B) 'Anatomy' of a simple neural circuit

with output principal neurons (P1-4) and two layers of intemeurons (11) and 12,3) (from Ref. 20). Hopfield and Tank 2° used this generalized circuit to compute solutions to problems of object recognition and memory. Note the functional (but not structural) similarity to the olfactory bulb circuit shown in Fig. 2. Abbreviations: 11,2,3, inhibitory neurons; P1,2,3,4,principal output neurons.

m/t cells. There is strong evidence that these connections are excitatory from a m/t cell to a pg cell and inhibitory from a pg back onto a m/t, thus serving as both inhibitory feedback and feedforward circuits modulating m/t output activity, although the suggestion that the pg cells might be excitatory has also been made38. These interactions form the first level in the bulb at which lateral interactions among m/t cells can take place via interneurons and they provide connections mediating a second pattern of divergence and convergence (see Fig. 2). Mitral/tufted cells also interact through lateral connections at the next level by forming reciprocal excitatory/inhibitory synaptic connections with another interneuron, the granule cell (excitatory from m/t to granule cell and inhibitory from granule cell to m/t cell). The spiny dendrites of granule cells are shared by many m/t cells and thus provide a third pathway for convergent/divergent communication. Recurrent collaterals arising from m/t axons extend back into the external plexiform layer. 83

It is not known how the array of action potentials triggered by the odor stimulus and now distributed across the olfactory nerves is processed by the circuitry described above. It is commonly observed, however, that single m/t cell responses appear as complex patterns composed of temporal sequences of depolarization and hyperpolarization that arise from the interactions of the excitatory and inhibitory synapses in the bulb~9. Presumably, these temporal activity patterns are dispersed across the population of output axons of the m/t cells (labeled 'mot' in Figs 1, 2, 3B) and, as in the olfactory nerve, this spatiotemporal array now carries re-encoded information about the stimulus to the next levels of integration, the olfactory cortices (anterior olfactory nucleus, olfactory tubercle, piriform cortex, amygdala). One manifestation of convergent and divergent pathways is seen in the properties of the excitatory and inhibitory receptive fields of m/t cells. For any one m/t cell, relatively restricted but different receptive fields are seen for different odors that trigger excitatory responses, and more widely dispersed fields are seen in cells showing sustained inhibition4°. Thus, an odorant can exert different influences on single m/t cells by interacting with receptor cell populations that have different distributions throughout the mucosa and that have different patterns of connections with the bulb (see Ref. 26 for discussion). Figure 3 shows examples of dispersed activity triggered in the bulb of the salamander in 2-DG activity maps of en face views of the glomerular layer (Fig. 3A), and in voltage-sensitive dye responses seen after electrical (Fig. 3B) and odor stimulation (Fig. 3C). The 2-DG maps illustrate the large regions of the glomerular layer that show aggregate activity (right panel) after 90 min of stimulation with a single odorant compound (amyl acetate) in comparison with the relatively small amount of activity generated by normal respiration of clean air (left panel). Better temporal resolution is obtained in voltage-sensitive dye recordings that not only co~ffirm the observations from 2-DG mapping that depolarization is widely distributed in each of the bulbar layers but also illustrate how the activity progressively travels from one bulbar layer to the others. Figures 3B and C show the first several, consecutively recorded, frames (33 ms/frame) of responses after stimulation. While the voltage-sensitive dye tracks membrane potential changes in both directions, hyperpolarization is not displayed in these records. In Fig. 3C it can be seen that stimulation with three different odorants elicits responses that have different latencies, different amplitudes, and that are differently distributed across each of the bulbar layers. Other kinds of distributed activity are also observed in the form of intrinsic and evoked EEG oscillations. These have been explored extensively in the bulb of mammals 4~ and other species 4~, although they have not been routinely reported in the salamander. In vertebrates, these oscillations often occur at about 40 Hz and are of particular interest because they have been seen in other kinds of cortical tissue and might serve as ways of synchronizing coordinated activity in many elements that act in parallel Among the many unanswered questions about the responses to odors are how they relate to physical and chemical characteristics of the stimuli, over what 84

time periods they remain stable, how single cell responses relate to them and specifically how the structure of the circuit gives rise to these responses. One expects that an encoded characterization of the stimulus lies embedded in these spatial and temporal patterns. These kinds of global response data, in conjunction with responses recorded in single cells, should provide information that could be used to design realistic computer simulations.

Odor processing and computer simulation The development of simulation models that incorporate global as well as unitary response characteristics of the bulbar circuitry, should provide some insight into how dispersed response patterns are generated and how they relate to odorant stimulus characteristics. A number of models have been developed that include various features of olfactory function (see Refs 43-45, Ref. 24 for the vertebrate olfactory bulb, Ref. 22 for the snail olfactory lobe, Ref. 46 for the piriform cortex, and also Ref. 23 for review). Each of these approaches has sought to adhere, to varying degrees, to the known structure of the system as well as to encompass particular characteristics of its function. Figure 4 illustrates two generalized forms of neural networks as described by Rumelhart et al. 47 (A) and Hopfield and Tank 2° (B). The similarity between the bulb and these circuits has been alluded to by Hopfield and Tank. These networks are specifically designed to allow a pattern of activity in an input array to generate a particular pattern of activity in an output array, by appropriately weighting different interconnections among many elements that have been activated in parallel. In Fig. 4A an intermediate set of elements, called 'hidden units', serves as a level at which the flexibility of this interconnectedness might be implemented. The generation of distributed patterns of output from distributed patterns of input appears to be precisely what is occurring along the olfactory pathway. In these models, the appropriate weights of interconnections required to relate a given input pattern to a given output pattern can be determined by a number of algorithms 4s. These procedures generally function by training the network on known sets of input and output patterns using iterative methods. Many of these methods function by examining the differences between an output pattern generated by randomly established interconnections and the desired target output pattern, re-adjusting the interconnection weights, calculating a new output pattern, making an estimate of the difference between the new pattern and the target, re-adjusting the connections, recalculating the output pattern, and so on. Given a number of sets of input and output patterns that are known to relate to one another, such iterative methods can be used to derive principles that underlie the relatedness of all the patterns. For example, such approaches have been used to examine the relations between tertiary protein structure and amino acid sequences 49 and between the written word and spoken language s°. Since these kinds of neural networks can be used to examine principles that emerge from relationships occurring between input and output patterns, such approaches would seem to be particularly useful for examining odorant-response pattern relationships in TINS, Vol. 14, No. 2, 1991

the olfactory system. If the structure of the interconnections of the network is matched to what is known about the real circuitry of the bulb, it might be possible to elucidate the neurophysiological substrate within the bulb on which odorant discrimination depends. To fashion such networks into realistic representations of the olfactory pathway, one needs to incorporate not only what is known about the structure of the circuit, but also to include the characteristics of actually observed input and output activity patterns. It is now possible to incorporate activity patterns from living brain structures into such bulbar models. Freeman has done this using EEG data 44 and a preliminary study by Sejnowski et al. 51 used data from 2-DG mapping. Data obtained from voltagesensitive dye studies might be especially useful in this regard, since it should be possible to incorporate into the model both temporal and spatial patterns of direct measures of the depolarizations and hyperpolarizations that are seen in the dye records, constraining the connections in the network to conform to the known anatomy of the circuit, and constraining the output from the model to conform to the observed responses in the dye records. Indeed, data from voltage-sensitive dye studies have recently been used in modeling studies of the visual cortex s2. Analyses of how odorant information may be internally represented in 'hidden units' of such networks (Fig. 4A) might provide insight into those stimulus characteristics that are critical for the discriminations made by the network. This in turn might clarify how the synaptic interactions in the bulb could serve as such 'hidden units' in the real circuits. This approach has recently been applied using voltage-sensitive dye recordings after controlled odor stimulation s3. Although much of the early promise of neural network architecture is still to be realized, we are only at the beginning of the development of computational systems that function with the complexity and flexibility even remotely reminiscent of brain circuits. There are exciting times ahead as we learn from comparisons made between simulated and real brain circuits, each using the other to guide understanding.

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11 Slotnick, B. M., Graham, S., Laing, D. G. and Bell, G. A. (1987) Brain Res. 417, 343-346 12 Graziadei, P. P. C. and Monti Graziadei, G. A. (1978) in Handbook of Sensory Physiology (Vol. 9) (Jacobson, M., ed.), pp. 55-83, Springer-Verlag 13 Pedersen, P. E. and 81ass, E. M. (1982) Dev. PsychobioL 15, 349-355 14 Wysocki, C. J., Domes, K. M, and Beauchamp, G. K. (1989) Proc. Natl Acad. Sd. USA 86, 7976-7978 15 Kauer, J. S. (1974) J. PhysioL 243,695-716 16 Meredith, M. and Mou[ton, D. G. (1978) J. Gen. Physiol. 71, 615-643 17 Malt, R. G. (1982) J. Physiol. 326, 341-359 18 Wellis, D. P., Scott, J. W. and Harrison, T. A. (1989) J. Neurophysio/. 61, 1161-I 177 19 Orbach, H. S. and Cohen, L. B. (1983) J. Neurosci, 3, 2251-2262 20 Hopfield, J. J. and Tank, D. W. (1986) Science 233,625-633 21 Poggio, T. and Edelman, S. (1990) Nature 343,263-266 22 Gelperin, A., Tank, D. W. and Tesauro, G. (1989) in Neural Models of Plasticity: Experimental and Theoretical Approaches (Byrne, J. H. and Berry, W. O., eds), pp. 133-I 59, Academic Press 23 Haberly, L. B. and Bower, J. M. (1989) Trends Neurosci. 12, 258-264 24 Schild, D. (1988) Biophys. J. 54, 1001-1011 25 Mason, J. R. and Stevens, D. A. (1981) Physiol. Behav. 26, 647-653 26 Kauer, J. S. (1987) in Neurobiology of Taste and Smell (Finger, T. E. and Silver, W. L., eds), pp. 205--231, John Wiley & Sons 27 Kauer, J. S. (1981) Anat. Rec. 200, 331-336 28 Dubois-Dauphin, M., Tribollet, E. and Dreifuss, J. J. (1981) Brain Res. 219, 269-287 29 Land, L. J. and Shepherd, G. M. (1974) Brain Res. 70, 506-510 30 Mori, K., Fujita, S. C., Imamura, K. and Obata, K. (1985) J. Comp. NeuroL 242,214-229 31 Astic, L., Saucier, D. and Holley, A. (1987) Brain Res. 424, 144-152 32 Haberly, L. B. (1985) Chem. Senses 10, 219-238 33 Hornung, D. E., Lansing, R. D. and Mozell, M. M. (1975) Nature 254, 617-618 34 Moulton, D. G. (1976) Physiol. Rev. 56, 578-593 35 Pevsner, J., Sklar, P. B. and Snyder, S. H. (1986) Proc. Nat/ Acad. Sci. USA 83, 4942-4946 36 Sicard, G. and Holley, A. (1984) Brain Res. 292,283-296 37 Mori, K. (1987) Prog. NeurobioL 29, 275-320 38 Martinez, D. P. and Freeman, W. J. (1984) Brain Res. 308, 223-233 39 Hamilton, K. A. and Kauer, J. S. (1989) J. Neurophysiol. 62, 609-625 40 Kauer, J. S. and Moulton, D. G. (1974) J. Physiol. 243, 717-737 41 Grajski, K. A. and Freeman, W. J. (1989) Behav. Neurosci. 103, 790-804 42 Gelperin, A. and Tank, D. W. (1990) Nature 345,437-440 43 Rail, W. and Shepherd, G. M. (1968) J. Neurophysiol. 31, 884-915 44 Freeman, W. J. (1987) BioL Cybern. 56, 139-150 45 Li, Z. and Hopfield, J. J. (1989) BioL Cybern. 61, 379-392 46 Granger, R., Ambros-lngerson, J., Staubli, U. and Lynch, G. (1988) in Neuroscience and Connectionist Theory (Gluck, M. and Rumelhart, D., eds), pp. 1-45, Hillsdale: Erlbaum Associates 47 Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) in

Acknowledgements This work was supported by grants from the N/H, Pew Freedom Trust, the Office of Naval Researchand by the Dept of Neurosurgery, NEMC I thank Drs Barbara Ta/amoand Dona Chikaraishifor critically reading the manuscript.

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