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448 Gustatory processing is dynamic and distributed Donald B Katz*, Miguel AL Nicolelis† and Sidney A Simon† The process of gustatory coding consists of neural responses that provide information about the quantity and quality of food, its generalized sensation, its hedonic value, and whether it should be swallowed. Many of the models presently used to analyze gustatory signals are static in that they use the average neural firing rate as a measure of activity and are unimodal in the sense they are thought to only involve chemosensory information. We have recently elaborated upon a dynamic model of gustatory coding that involves interactions between neurons in single as well as in spatially separate, gustatory and somatosensory regions. We propose that the specifics of gustatory responses grow not only out of information ascending from taste receptor cells, but also from the cycling of information around a massively interconnected system. Addresses *Volen Center for Complex Systems, Department of Psychology, Brandeis University, MS 062, Waltham, MA 02454-9110, USA; e-mail: [email protected] † Departments of Neurobiology and Neuroengineering, Duke University Medical Center, Durham, NC 27710, USA Correspondence: Donald B Katz Current Opinion in Neurobiology 2002, 12:448–454 0959-4388/02/$ — see front matter © 2002 Elsevier Science Ltd. All rights reserved. Published online 12 July 2002 Abbreviations AFP across-fiber patterns CN central nucleus GABA γ-amino butyric acid GC gustatory cortex LL labeled lines NST nucleus of the solitary tract PbN parabrachial nuclei of the pons PSTH peri-stimulus time histogram Functional anatomy of the gustatory system Figure 1a shows a schematic diagram of the principle gustatory pathways [2,3]. Transduction of chemical information occurs in the oral cavity when chemicals make contact with taste receptor cells [4••,5••]. Primary gustatory neurons course within the CNS cranial nerves VII, IX and X [6] to the nucleus of the solitary tract (NST), which in turn transmits information to the parabrachial nuclei of the pons (PbN). From the brainstem, taste information is transmitted to the thalamocortical system, amygdala and hypothalamus. Such descriptions of the circuitry usually ignore the fact that the gustatory system is made-up of networks of feedforward and feedback pathways. Figure 1b presents a simple reconceptualization of the system with the goal of assisting the reader in understanding the dynamic and distributed nature of gustatory processing. The gustatory system is separated into interacting taste areas (e.g. gustatory cortex [GC] and NST). In each area, the presence of local connections among primary neurons and interneurons imply that between-neuron interactions should occur within each region. Feedback pathways, meanwhile, suggest that interactions should occur between, for example, the amygdala and GC, and between each of these forebrain areas and both the NST and PbN (Figure 1a). Finally, projections from somatosensory neurons throughout the oral cavity, and from visceral neurons in the gut, intermingle with gustatory neurons in the NST [7] and GC [8], suggesting that interactions should occur between taste and other systems. Because of such interactions, gustation might be expected to involve time-varying responses, as does processing in other sensory systems that contain convergence and divergence of pathways (e.g. [9–11]). Currently debated models of taste coding are static and noninteractive Introduction The gustatory system has evolved to detect and discriminate between foods, to select nutritious diets, and to initiate, sustain and terminate ingestion [1]. These processes evolve over several seconds and involve the integration of multiple sources of information. In this review, we discuss the neural system that underlies taste behaviors, focusing particularly on the time-varying nature of taste neural responses and the neural interactions that may give rise to such time-varying responses. First, we explain how the convergence of distributed pathways throughout the gustatory neural circuitry leads to dynamic responses. Next, we show that the currently debated static models of taste coding cannot account for the dynamic and interactive aspects of gustatory neural processing. Finally, we present recent data confirming that gustation is dynamic and distributed, and argue that these data require a more general systemic theory of gustatory coding. The long-standing debate over the nature of gustatory neural coding has primarily addressed the question ‘Are taste stimuli coded by labeled lines (LL), by across-fiber patterns (AFP), or by some combination of the two?’ Discussions of these models focus on whether dedicated sets of neurons signal the presence of a particular taste component, or whether tastes are represented in the ‘landscape’ of responses across an entire neural population (see [3]). Both of these models have admirably described how patterns in ‘taste space’ are arranged (by the clumping of similar tastes) and how they may change under pharmacological (e.g. amiloride, gymnemic acid) and physiological (e.g. emesis) manipulations [12–14]. The difference between the LL and AFP theories is less than might be supposed. Consider a hypothetical ensemble of 27 neurons whose activity is described in terms of mean firing rates, and that are assumed to represent the gustatory Gustatory processing is dynamic and distributed Katz, Nicolelis and Simon 449 Figure 1 (b) (a) Cortex (GC, OFC) VPMpc thalamus Gustatory system Main taste neuroaxis Taste area one (e.g. GC) Taste area two (e.g. NST) Amygdala Brain stem (PbN, NST) Hypothalamus Somatosensory/visceral systems Information from the oral cavity and gut (cutaneous, thermal, nociceptive) CN VII CN IX CN X Current Opinion in Neurobiology The gustatory system viewed as interacting distributed neural networks. (a) A schematic of gustatory neural circuitry, showing input to the brain stem nuclei from cranial nerves (CNs) VII, IX and X, and the most prominent CNS pathways. Note the many ‘double-headed’ arrows denoting reciprocal (i.e. both feedforward and feedback) connectivity. OFC, orbitofrontal cortex; VPMpc, ventroposteriomedial thalamus parvocellular region. (b) The gustatory system as depicted simply in terms of intra-area and between-area interactions. Each gustatory nucleus (in this example GC and NST) contains networks of interconnected neurons that can be expected to modulate each others firing rates through time. The two nuclei are themselves reciprocally interconnected, so that the outputs of each nucleus can be expected to affect activity in the other. Finally, nuclei comprising the gustatory system interact at all levels with inputs from the somatosensory and visceral systems. system’s response to a lingual administration of a tastant (e.g. 0.1 M NaCl). According to AFP theory (Figure 2a,b), the tastant is identified as 0.1 M NaCl because of the particular distribution of responses across the entire ensemble. According to LL theory, the subset of the neurons having the strongest activation (neurons 8, 9, 10 and 17) will reflect the particular taste of 0.1 M NaCl. In other words, the simplest version of LL theory is a special case of AFP theory, in which an arbitrary threshold causes one set of fibers to be described as ‘on’ and the others to be described as ‘off’ (compare Figure 2b,c). Time-varying gustatory responses The two theories are also similar in that, in their simplest forms, neither incorporates two related aspects of gustatory electrophysiology. The first is the occurrence of timevarying responses. As both models are based on firing rate averages across 3–5 s, neither takes into account information in the spike trains, such as adaptation, bursting, or poststimulus response dynamics. In addition, neither theory allows for the interactions between neurons that would be expected to induce temporal structure in the neural responses (for recent reviews, see [15,16]). To the extent that these phenomena occur, we must seek models that are dynamic and interactive at multiple levels and timescales. Recent evidence (given below) indicates that gustatory neural responses are dynamic and affected by interactions with other neurons within the same nucleus, with other gustatory nuclei and with ‘non-gustatory’ systems (Figure 1b). Dynamic properties have recently been recognized in the responses of neurons in several sensory systems, including some, such as gustation, in which responses had previously been described in terms of a single value [17••,18,19]. The investigation of the role of time in gustatory responses dates back to the recognition that the firing rates of primary gustatory neurons peak and then decrease as the system adapts to prolonged application of gustatory stimuli [20–22]. By the early 1980s, however, researchers also began considering the possibility that tastant-specific timecourses might provide information for the identification of tastants. Di Lorenzo and Schwartzbaum [23] showed that moderate increases in the information content of NST spike trains in responses to NaCl and sucrose can be traced to differences in the time-courses of these neurons’ responses. More recently, researchers have used mathematical procedures related to fuzzy set theory to identify a set of temporal components in NST gustatory responses, showing that membership in a taste category may be based on ‘family resemblance’ related to the placement of taste responses on multidimensional continua, rather than on ‘crisp’ coding [24]. Different combinations of these components explained cell-specific time-courses, but there was no attempt to identify the sources of the time-courses. To explain the processes contributing to the genesis of response dynamics, we performed a detailed analysis of 450 Sensory systems Figure 2 (a) Average firing rate 40 30 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 (b) ‘Portrait’ of NaCl firing rates as interpreted by AFP decoder (c) ‘Portrait’ of NaCl firing rates as interpreted by LL decoder A conceptualization of the static across-fiber pattern and labeled-line models of gustatory coding. (a) Hypothetical responses given as average firing rate (over 5 s) minus the background activity for 27 neurons to a given a taste stimulus (e.g. 0.1M NaCl). The dark bars represent the response of those neurons that responded most strongly to NaCl. The dashed line represents a hypothetical threshold of firing rate. (b) A re-presentation of (a) with color intensity coding the firing rate for each of the 27 neurons (red, excitation; blue, inhibition), representing how an AFP ‘decoder’ might read the responses. (c) Another re-presentation of (a), representing how a LL decoder might read the responses. Black denotes that these neurons’ responses are ‘on’ (as set by the threshold) and white denotes ‘off’. Current Opinion in Neurobiology response dynamics in single-neuron GC ensembles in awake rats [25••]. We found that GC responses exhibit complex and neuron-specific time-varying responses to gustatory stimuli (Figure 3a). Analysis of the spectral and stimulus information content of these GC responses revealed that the timings of firing rate changes were driven, in part, by three distinct contributions. The earliest was a somatosensory response to the tastant striking the tongue, the second had a chemosensory origin and the third was a combination of the two, related to the palatability of the tastant (Figure 3b). In summary, the most parsimonious explanation of the GC responses is that neural networks in different cortical areas contribute to the GC responses at different times (it is worth noting that the ideas advocated here are compatible with the recent writings of Erickson, one of the pioneers of taste coding theory, who has long since abandoned the version of AFP presented above). Temporal responses evoked by tastants have also been investigated in humans using magnetoencephalography [26], a noninvasive technique that combines the temporal sensitivity of elecroencephalography with the spatial resolution of functional magnetic resonance imaging. These experiments have revealed that, over the course of 1.5 s of processing gustatory information, gustatory cortical regions may be activated repeatedly. The authors concluded that the necessarily time-extensive process of gustation requires time-varying neural activation, an hypothesis fully consistent with our dynamic distributed model of gustatory coding. Interactions in gustatory responses: I. Within-area interactions Over the past decade, interactive processing within neural populations (typically measured as peaks in cross-correlograms or cross-spectra for pairs of neurons) has received serious consideration as a potential mechanism of neural coding. Much of the attention paid to interactive neural processing has been directed towards the search for functionally significant patterns of ‘synchrony’, that is the near-simultaneous (timescale of 1–10 ms) firing of individual action potentials between neurons. Several recent studies have demonstrated that GC or NST neuron pairs may respond synchronously to stimulation with particular subsets of tastants [27–30]. These interactions typically occur only between pairs of neurons separated by <100 µm, and are most prevalent when the neurons in the pair produce similar responses to the proffered tastant [31•]. Such data may reflect the existence of direct connections between the recorded pair, and have been interpreted as evidence that nearby GC neurons form part of columnar processing units [31•]. On a broader (hundreds of milliseconds) timescale, tastespecific cross-correlations of >100 ms duration were found Gustatory processing is dynamic and distributed Katz, Nicolelis and Simon 451 Figure 3 (a) sp/s Gustatory cortical responses are dynamic and multimodal. (a) Peri-stimulus time histograms (PSTHs) of a single GC neurons’ responses to NaCl, citric acid, sucrose and quinine hydrochloride (Q-HCl). The PSTHs exhibit time-varying responses both within and between epochs (i.e. somatosensory contact of the tastant, chemosensory processing of the tastant and multi-sensory coding of palatability). Periods of inhibitory firing rate change are noted in blue/gray, and a period of excitatory firing rate change is shown in orange. Post-stimulus times are on the abscissae and the response magnitude in spikes per second (sp/s) is shown on the ordinates. (b) A schematic of the ‘epochs’ of tastant responses in GC neurons obtained from analysis of the GC responses. Somatosensory responses occur within 200 ms of tastant delivery, as the tastant strikes the tongue. Chemosensory responses occur thereafter. Approximately 1 s or more following tastant delivery, a new somatosensory contribution that is related to the production of palatability-specific orofacial behaviors comes into play, as the stimulus’ hedonic content is now coded. Reproduced with permission from [25••]. NaCl Citric acid Sucrose Q-HCl 40 20 0 -1 0 2 -1 0 2 -1 0 2 -1 0 2 Time post stimulus (s) (b) ‘Epochs’ Contact Chemosensation Palatability Input Chemosensory Somatosensory between relatively large percentages of GC neurons in awake rats [32••]. Figure 4a shows an example of such cross-correlations. The neuron pair analyzed here crosscorrelated strongly (and negatively) only in the presence of nicotine (orange line) and citric acid (green line). Peaked cross correlations were not found for sucrose (black), NaCl (maroon) or quinine (dark blue). When cross-correlations are taken into account, 75% of the neurons in GC are involved in gustation. In contrast, if only average firing rates are considered, only 10–14% of the GC neurons contribute to gustatory processing [25••,33]. These cross-correlations, which are found even in crosshemispheric pairs of GC neurons, may reflect state–rate transitions in large neural networks associated with the perceptual processing of tastants (Figure 1b; [15,16,34]). Furthermore, these between-neuron interactions may explain the previously described time-varying GC response properties (Figure 3a; [25••]). Recent simulations of the insect antennal lobe support the hypothesized relationship between between-neuron interactions and single-neuron rate changes on a timescale similar to those observed in GC neurons [35••,36]. In a similar manner, γ-amino butyric acid (GABA)ergic synapses have been shown to play a role in shaping gustatory responses in the GC and NST [37,38]. Applications of the GABAA antagonist bicuculline unmasked taste-related activity in GC and changed the tastant causing the strongest (best) response (Figure 4b). In summary, gustatory responses are formed of the interactions between neurons within individual nuclei in the CNS (Figure 1b). 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Time post stimulus (s) Interactions in gustatory responses: II. Between-region interactions In addition to interactions within single areas, interactions between regions also modulate taste responses (see also [39]). Indeed, recent evidence showed that feedback connections between GC and NST modulate NST activity via both excitatory pathways and GABAergic synapses [40•]. Figure 4c shows an example of a GC–NST interaction. It is clear that cortical activation (electrical stimulation) may excite or inhibit NST activity, and that infusion of bicuculline in the NST blocks the inhibitory impact of GC stimulation. These data suggest that projections from the GC directly or indirectly influence GABAergic interneurons in the NST. Additional evidence for the interaction between different cortical areas comes from studies showing that activating the central nucleus (CN) in the amygdala modulates gustatory responses in the PbN [41•]. Figure 4d shows a single PbN neuron’s response to 0.1 M NaCl in the absence (upper trace) and presence (lower trace) of a periodic stimulation of the CN. Although amygdalar stimulation usually inhibited responses to all tastants, in some cases the impact was excitatory whereas in others it was taste specific. In summary, these data suggest that theories of gustatory processing should incorporate the real-time modulation of taste responses by both within-region neural networks and between-region convergence of activation. Interactions in gustatory responses: III. Input from other systems Early electrophysiological studies reported converging projections from gustatory and lingual somatosensory origins 452 Sensory systems Figure 4 (a) (b) Pre Bicuc Post 0.1 Correlation (r) N 0.0 S -0.1 H -0.2 Q -1.0 -0.5 (c) 0 Lag (s) Stim + bicuc 1.0 Stimulus on times (d) sp/s w/o stim Stim 0.5 w/ stim NaCl on: Stim on: Time Evidence for intra-area (a,b) and between area (c,d) interactions in gustatory coding. (a) Cross-correlations for two GC neurons in response to six different tastants (lag [s] is on the abscissa, correlation [r] is on the ordinate). Significant interactions can be seen in response to nicotine (thick orange line) and citric acid (thick green line), but not to water (light blue), NaCl (maroon), sucrose (black), or quinine (dark blue). (b) PSTHs for a single GC neuron in response to NaCl (N), sucrose (S), hydrochloric acid (H), and quinine (Q) depend on inhibition in the cortical neural network. The first column shows the responses under control conditions (Pre): note the lack of response to quinine. The middle column shows the responses to the same stimuli during infusion of GABA antagonist bicuculline (Bicuc): note that the time courses of responses have changed, that the stimulus causing the largest response has changed, and that a response to quinine has been unmasked. The third column shows the response returning to baseline following the removal of bicuculline (Post). Bar indicates duration of the stimulus. (c) Neural activity in NST is affected by feedback from GC. The responses of two NST neurons to electrical stimulation of GC are shown (time is on the abscissa, response magnitude [spikes per second, sp/s] is on the ordinate). In one neuron (top), GC stimulation caused an excitatory response, whereas in the other (bottom) GC stimulation caused an inhibitory response. The inhibitory response was blocked by bicuculline, suggesting inhibitory feedback is caused by GC activation of GABA-containing neurons. (d) Taste responses in the pons (parabrachial nuclei) are affected by electrical stimulation of the amygdala. In each row, vertical lines represent single action potentials. Time progresses to the right. The top row shows this neuron’s response to the application of NaCl without amygdala stimulation. The bottom row shows the same neuron’s response to NaCl in the presence of stimulation; each burst of stimulation lasted for one second. The stimulation reduced the NaCl response, but did so in a temporally complex manner. (a) reproduced with permission from [32••], (b) reproduced with permission from [37], (c) reproduced with permission from [40•] and (d) reproduced with permission from [41•]. in several relays of the gustatory pathway, especially at the cortical level, where somatosensory and taste responses are found in intermingled cells or even within the same cells [2,42,43]. In this regard, it may not be so surprising to learn that mechanically stimulating the tongue can produce ‘taste phantoms’ [44] and that temperature changes on the tongue can induce or modulate specific tastes [45••]. In addition, behavioral studies have shown that palatability can be modulated by trigeminal input [46]. Nevertheless, there is a paucity of studies showing how the somatosensory system may modulate electrophysiological responses to gustatory stimuli. In one such study, chorda tympani responses to tastants have been modulated by electrical stimulation of the lingual branch of the trigeminal nerve [47] and, in another study, the trigeminal stimulant capsaicin has been shown to decrease chorda tympani responses to NaCl [48]. The effects of visceral stimulation on gustatory responses have been demonstrated in studies of conditioned taste aversion [49,50], and in a study showing that NST responses to tastants are modulated by gastric distension [51]. In addition, single unit responses from neurons from the caudolateral part of the orbital frontal cortex markedly decrease in a taste-specific fashion as monkeys are fed to satiety [52]. Although it is unclear where the convergences Gustatory processing is dynamic and distributed Katz, Nicolelis and Simon take place, what is clear is that gustation depends on convergence between the taste system and the somatosensory/ visceral system. Conclusions We have attempted to justify the reasons for moving beyond static and unimodal models of gustatory coding towards models in which processing occurs in time, is multimodal and involves interactions between neurons in the same and in spatially separate gustatory regions. We propose that the specifics of gustatory responses grow not only out of information ascending from taste receptor cells but rather from the cycling of information around a massively interconnected system. Acknowledgements We are grateful to Robert Erickson for many fruitful discussions of data and theory. We also acknowledge support from NIH grants DC-00403 (DBK), DC-01065 (SAS), and DE-11121 (MALN), and by Philip Morris Incorporated. References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: • of special interest •• of outstanding interest 1. Dethier VG: A comparative role of taste in food intake: a comparative view. In Mechanisms of Taste Transduction. Edited by Simon SA, Roper SD. Boca Raton: CRC Press; 1993:3-25. 2. Norgren R: Gustatory system. In The Human Nervous System. Edited by Paxinos G. New York: Academic Press; 1990:845-861. 3. Smith DV, St John SJ: Neural coding of gustatory information. Curr Opin Neurobiol 1999, 9:427-435. 4. •• Chandrashekar J, Mueller KL, Hoon MA, Adler E, Feng L, Guo W, Zuker CS, Ryba NJ: T2Rs function as bitter taste receptors. Cell 2000, 100:703-711. This is the first description of the G-protein coupling and function of some T2Rs as bitter taste receptors. This paper also describes some of the polymorphisms found in the cycloheximide receptor of some mouse strains that have a lesser sensitivity for this compound. 5. •• Gilbertson TA, Boughter JD Jr, Zhang H, Smith DV: Distribution of gustatory sensitivities in rat taste cells: whole-cell responses to apical chemical stimulation. J Neurosci 2001, 21:4931-4941. 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When time-varying response properties were taken into account, the percentage of GC neurons identified as taste-specific jumped to 41% (compared to 14% when only overall average responses were considered). The observation of three temporal epochs map remarkably well onto those discerned via mathematical analysis of NST data (see Figure 3 in [24]). The authors analyzed activity in the primary somatosensory cortex to disassociate chemosensory from somatosensory responses. 6. Travers SP, Pfaffmann C, Norgren R: Convergence of lingual and palatal gustatory neural activity in the nucleus of the solitary tract. Brain Res 1986, 365:305-320. 26. Kobayakawa T, Ogawa H, Kaneda H, Ayabe-Kanamura S, Endo H, Saito S: Spatio-temporal analysis of cortical activity evoked by gustatory stimulation in humans. Chem Senses 1999, 24:201-209. 7. Whitehead MC, Frank ME: Anatomy of the gustatory system in the hamster: central projections of the chorda tympani and the lingual nerve. 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Yokota T, Eguchi K, Satoh T: Correlated discharges of two neurons in rat gustatory cortex during gustatory stimulation. Neurosci Lett 1996, 209:204-206. 11. Ringach DL, Hawken MJ, Shapley R: Dynamics of orientation tuning in macaque primary visual cortex. Nature 1997, 387:281-284. 31. Yokota T, Satoh T: Three-dimensional estimation of the distribution • and size of putative functional units in rat gustatory cortex as assessed from the inter-neuronal distance between two neurons with correlative activity. Brain Res Bull 2001, 54:575-584. The authors of this paper measured the spatial and response relationships between GC neuron pairs showing tight (i.e. probably monosynaptic) connections between neurons. These careful recordings revealed that direct 12. Minear MM, Hammack SE, Lundy RF, Contreras RJ: Amiloride inhibits taste nerve responses to NaCl and KCl in Sprague-Dawley and Fischer 344 rats. Physiol Behav 1996, 60:507-516. 454 Sensory systems connections between GC neurons are vanishingly rare beyond distances commensurate with the size of cortical columns, but also that the efficacy of even direct connections could be modulated in a taste-specific manner. 32. Katz DB, Simon SA, Nicolelis MA: Taste-specific neuronal •• ensembles in the gustatory cortex of awake rats. J Neurosci 2002, 22:1850-1857. The authors of this paper produced several simulations in order to test the various possible sources of GC neuron–neuron interactions. The observed interactions were not caused by between-trial magnitude coupling, onset coupling, or spike-to-spike coupling. Instead, they depended on coupling between single-trial rate changes, and to a significant extent on coupling in sudden changes in rate. These cross-correlations provided additional information as to tastant identity. 33. 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