Neural Coding in the rNST

David V. Smith; Christian H. Lemon

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Bradley RM, editor. The Role of the Nucleus of the Solitary Tract in Gustatory Processing. Boca Raton (FL): CRC Press/Taylor & Francis; 2007.

The Role of the Nucleus of the Solitary Tract in Gustatory Processing.

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Chapter 5Neural Coding in the rNST

David V. Smith and Christian H. Lemon.

5.1. INTRODUCTION

The gustatory system in mammals provides sensory input that is critical for the regulation of ingestive behavior and the avoidance of toxic substances. Taste serves a unique role among sensory systems in the extent to which it interfaces with neural substrates of reward and motivation [1]. For example, sweet- and bitter-tasting stimuli produce inherent preference and avoidance, respectively, and sweet-tasting stimuli can serve as effective reinforcers. The anatomy of the taste system reflects its dual role as both a discriminative system, designed to determine subtle differences in taste quality and intensity, and a motivational one, which underlies the acceptance and rejection of potential foods. Anatomically, the taste system is situated between the external environment and the internal milieu, making taste a rostral extension of the visceral afferent system [2].

The rostral nucleus of the solitary tract (rNST) in the medulla lies at the interface between taste receptors and the brain. Gustatory neurons in the rNST receive input from the chorda tympani and greater superficial petrosal nerves, which are branches of cranial nerve VII, the glossopharyngeal nerve (IX), and the vagus nerve (X). The chorda tympani nerve innervates taste bud cells of the fungiform papillae on the anterior tongue, whereas the greater superficial petrosal nerve innervates those in palatal epithelia. The glossopharyngeal nerve relays taste information from the posterior tongue to the rNST. The vagus nerve innervates receptor cells found near the epiglottis. Gustatory neurons in the rNST serve the critical function of integrating and encoding information received from taste receptors and routing this information to higher centers involved with motivated behavior and perceptual processing. The NST also projects information to nearby structures in the brainstem that are involved in mediating oromotor responses. Many factors influence how rNST neurons respond to taste stimuli and also how information about tastants could be encoded in their spike outputs.

5.2. TASTE RESPONSES OF rNST NEURONS

Gustatory neurons in the rNST are typically more broadly tuned to taste stimuli than peripheral fibers and often respond to somatosensory inputs, such as tactile and temperature stimulation, as well [3,4]. Neurons in the rNST receive converging input from peripheral gustatory axons [5], and these inputs can arise from separate taste bud populations, such as those on the anterior tongue and palate [6,7]. This convergence renders rNST neurons more broadly tuned than peripheral fibers to the extent that medullary taste cells are quite broadly responsive [3]. The responses of an NST neuron of the rat to several taste stimuli are shown in Figure 5.1. As predicted by the co-expression of T2r taste receptors for bitter stimuli in taste bud cells [8], the responses to bitter stimuli in the cell in Figure 5.1 and other bitter-sensitive NST cells are highly correlated [9], although these same neurons respond to stimuli representing other taste qualities as well, such as NaCl and HCl (Figure 5.1).

FIGURE 5.1

Response properties of an individual gustatory neuron in the rat NST. Oscilloscope sweeps show single-unit activity evoked by the application of several stimuli to the tongue and palate. In this figure, responses to stimuli classified as sweet, salty, (more...)

5.2.1. Breadth of Tuning

The breadth of tuning of sensory neurons is an important parameter that impacts the information-handling characteristics of such cells. In taste, breadth of tuning has been described by the number of stimuli among those representing the basic taste qualities (i.e., sweet, salty, sour, and bitter) that produce responses in a neuron [10,11]. However, this method depends heavily on the experimenter’s definition of a taste response. In 1979, Smith and Travers [12] introduced the entropy measure as a way to quantitatively describe the breadth of tuning of gustatory neurons. Entropy is calculated as

H = - K \sum_{i = 1}^{n} P_{i} log P_{i}

where H is a number describing the breadth of responsiveness, K is a scaling constant, and P_i is the proportional response to each of n stimuli. The P_i for each cell is calculated as the response to the ith stimulus expressed as a proportion of the total response to all four stimuli. This metric does not depend upon a response crierion. The value of H ranges from a minimum of 0 to a maximum of 1. A neuron that responds to only one stimulus out of four would achieve H = 0 (i.e., no uncertainty as to which stimulus produced a response), whereas a cell that responds equally well to all stimuli would result in H = 1 (i.e., maximum uncertainty). This metric has seen extensive use over the past 25 years in the study of both peripheral and central gustatory neurons (see Spector and Travers [13] for a thorough review of this literature).

Using the entropy measure, it has been shown that taste neurons in the rNST are generally more broadly tuned than peripheral gustatory axons [14]. For example, chorda tympani fibers that respond maximally to sucrose relative to Na⁺ salts, acidic, or bitter stimuli are the most narrowly tuned (mean H = 0.39, versus 0.59 for NaCl-optimal and 0.67 for HCl-optimal neurons). Yet at the level of the NST, sucrose-optimal neurons are found to be more broadly responsive (H = 0.59). This example shows how a single category of neuron in the periphery becomes systematically more broadly tuned at the level of the NST, likely as a result of convergent input from different kinds of peripheral fibers. Overall, cells in the central nervous system (CNS) of most species are relatively broadly responsive to stimuli representing different taste qualities.

5.2.2. Convergence of Peripheral Inputs

Fibers of the chorda tympani nerve typically innervate taste cells in multiple fungiform papillae, although the sensitivities of each branch appear to be highly similar, suggesting that taste axons are guided to make connections to particular receptors [15]. Thus, a neuron’s breadth of responsiveness does not appear to appreciably increase due to convergent input from separate axonal branches within the tongue. However, inputs from separate receptive fields are known to converge onto NST neurons [5], resulting in greater breadth of responsiveness of these cells. What is more, rat rNST neurons have been shown to receive input from completely separate receptor populations, such as the anterior tongue and palate [6,7].

Neurons of this sort often respond best to NaCl applied to the anterior tongue and sucrose applied to the palate. Convergence from different parts of the oral cavity onto cells in the hamster NST has also been described [16]. The increased breadth of responsiveness of NST neurons over peripheral fibers is likely due to convergent input from peripheral axons onto these cells. For example, oral application of the sodium channel blocker amiloride inhibits the robust responses to NaCl observed in sucrose-best neurons in the NST [17,18], although some sucrose-best chorda tympani fibers are quite narrowly tuned and display poor sensitivity to NaCl relative to NaCl-best fibers, which are amiloride sensitive.

The central representation of gustatory information at the level of the rNST is arranged in an orotopic fashion as a result of the diverse anatomical distribution of taste buds across the tongue, palate, and oropharyngeal and laryngeal epithelia and the innervation of these areas by three different cranial nerves [19,20]. Afferent neurons of the VIIth nerve, including those within the chorda tympani and the greater superficial petrosal nerves, terminate in the most rostral pole of the NST. Input from taste bud fields innervated by the IXth nerve overlaps this distribution but extends more caudally. The termination of the IXth nerve is overlapped and extended even more caudally by afferent terminals of the gustatory axons of the Xth nerve. This arrangement results in the oral cavity being roughly represented spatially within the NST, with input from the anterior tongue and palate most rostral and that from the epiglottis most caudal within the nucleus.

Electrophysiological recordings taken from gustatory nerves in rodents have revealed some differences in responsiveness to different stimuli across the various receptive fields, most notably a greater sensitivity to quinine and other bitter stimuli on the posterior tongue. This difference is also reflected in the responsiveness of cells in different regions of the NST [4,16]. This regional differentiation remains at higher levels of the gustatory pathway including the gustatory cortex, where input from the VIIth and IXth nerves is still somewhat segregated [21]. Such segregation could reflect different functional roles served by these inputs. Neurotomy experiments have revealed that gustatory information provided by the VIIth nerve, but not the IXth, appears to be critical for taste discrimination [22–24], whereas aversive taste reactivity (e.g., gapes) depends upon IXth nerve input [25]. That different nerves could serve different functional roles has been observed in other species such as fish, where the VIIth nerve is involved in food seeking and the IXth in ingestion [26,27]. Although the degree of anatomical overlap between cranial nerve inputs in mammals makes such a relationship more difficult to discern, similar functional dichotomy between VIIth and IXth nerve gustatory function may exist.

5.3. CATEGORIZING GUSTATORY NEURONS IN THE rNST

Much effort has been directed towards understanding whether there are types of gustatory neurons. Understanding this issue at the level of the rNST could provide insight into the functional organization of gustatory circuits in this structure. Different investigators have adopted different strategies to categorize gustatory neurons into purported functional groups.

5.3.1. Classification by Best-Stimulus and Multivariate Analysis

Most studies of information processing by gustatory neurons begin by categorizing cells on the basis of their taste responsiveness. There have been multiple methods used to do this, but the most widely used classification scheme is grouping neurons by their best-stimulus, as first described by Frank [28] for taste-responsive fibers of the chorda tympani nerve. Here, chorda tympani fibers were classified into groups based on which of four stimuli representative of the basic taste qualities (sucrose, NaCl, HCl, and quinine) were most effective when presented to the anterior tongue at midrange concentrations. When the stimuli were ordered along the abscissa from most to least effective, chorda tympani fibers peaked at a single point, with profiles indicative of sucrose- (S), NaCl- (N), or HCl- (H) best fibers. Quinine-best fibers were not evident in the chorda tympani nerve, although they are commonly observed in the glossopharyngeal nerve [29,30].

For a given neuron, if multiple stimuli are tested, one of them will undoubtedly be classified as “best.” However, what is interesting is that the taste sensitivity properties of neurons within a best-stimulus group are known to be relatively similar. One way of evaluating that similarity is to use a numerical taxonomic procedure, such as hierarchical cluster analysis, to investigate the similarities within and between neural groups (e.g., Rodieck and Brening [31]). Such an analysis was applied by Frank to the responses of hamster chorda tympani fibers to 13 stimuli, resulting in three types of neurons that correspond to the S-, N-, and H-best classifications [32]. This analysis demonstrates that neurons within a group are more similar to one another than they are to members of other groups and provides a quantitative basis for classifying neurons. Accordingly, multivariate analyses of the response profiles of hamster NST cells, even including responses from as many as 18 stimuli, resulted in classifications that were, to a large extent, predicted from the best-stimulus [33]. Earlier studies in rats, in which stimuli were not applied to the palate, did not present clear evidence for neural groups based on hierarchical clustering [34] and, in fact, argued against the existence of functional neural groups. Yet neuronal groupings derived from multivariate analysis of NST neurons in rats that correspond to S-, N-, and H-best classifications have been observed in more recent experiments where stimuli were applied to both the tongue and palate (also see Figure 5.2) [9,35,36].

FIGURE 5.2

Categorizing NST gustatory neurons. (A) The outcome of hierarchical cluster analysis as applied to categorize 76 NST neurons into groups based on similarities among their responses to (in M) 0.5 sucrose, 0.1 NaCl, 0.01 HCl, and 0.01 quinine-HCl. In this (more...)

5.3.2. Neuron Types: Physiological Significance

Support for categorizing neurons into groups based on their response properties also comes from analyses of physiological parameters of gustatory fibers and neurons. For example, oral application of amiloride has been shown to inhibit responses to NaCl in mainly N-best fibers of the chorda tympani [37]. The substantial response to NaCl in HCl-best fibers is completely unaffected by amiloride treatment. This segregation of amiloride-sensitive input to the NaCl- but not the HCl-best neurons is maintained at the level of the NST [38,39]. Amiloride-sensitive N-best neurons likely play a critical role in discriminating sodium taste, as application of amiloride to the tongue disrupts the performance of rats on a two-lever operant task in which they have been taught to discriminate NaCl from KCl [40,41]. The activities of NST neurons that respond best to sweets but are also sensitive to sodium salts may also be important for the coding of sodium taste, as oral application of amiloride also inhibits NaCl responses in S-best cells [17,18,42].

A correlate between physiology and tuning has also been observed in quinine-best neurons in the hamster NST. In a recent study, Cho et al [43]. found a substantial group of quinine-best neurons based on stimulation of the anterior tongue with equally effective concentrations of stimuli (in M): 0.032 sucrose, 0.032 NaCl, 0.0032 citric acid, and 0.032 quinine-HCl. In the hamster NST, quinine-best neurons are evident when this strong concentration of quinine is used to stimulate the anterior tongue. In this study, it was verified whether NST cells projected forward to the parabrachial nucleus, the second central synapse for taste information processing in rodents. Twenty-three quinine-best NST neurons were found that projected to the parabrachial nucleus. Interestingly, the mean conduction velocity of these quinine-best neurons was significantly lower than that of the other types of projection neurons, suggesting that the most quinine-responsive cells are a subset of smaller neurons. These data correspond well with the findings of Renehan et al. [44], showing that neurons responding specifically to quinine in the rat NST were significantly smaller than other cells. It is worthwhile to mention that at lower concentrations of quinine, such as those used to classify chorda tympani fibers [28], these cells would not necessarily have been classified as quinine-best, likely responding more strongly to NaCl or citric acid.

The classification of neurons by their best-stimulus does generally correspond to those categories derived from more quantitative techniques such as hierarchical cluster analysis, even when responses to more than four stimuli are analyzed. However, because of the relatively broader tuning of central taste neurons in comparison to peripheral gustatory fibers, the correspondence between a best-stimulus classification and a hierarchical cluster solution is not quite as tight in NST neurons as for peripheral fibers [33]. It is also important to consider that changes in stimulus concentration can impact the best-stimulus classification for many NST neurons. Yet the best-stimulus method of classifying gustatory neurons has been widely used and is sometimes argued as an organizing principle in this system.

5.4. MODULATION OF ACTIVITY IN THE rNST

The response profiles of NST gustatory neurons have been shown to be dynamic rather than static attributes of these cells. That is, the sensitivities of these neurons to tastants can vary depending on a range of factors, such as the level of inhibitory synaptic input [45] to the motivational state of the organism [46,47]. Here, we discuss data regarding how physiological variables can influence the response properties of NST neurons and also neural substrates that might be involved with such modulation.

5.4.1. Physiological Modulation of rNST Activity

The initiation and control of ingestive behavior is critically mediated by gustatory input, which guides the detection and consumption of nutritive foods and promotes the avoidance of toxic substances. Thus, the responsiveness of gustatory neurons to tastants is likely a determining factor as to whether stimuli will be ingested or avoided. Various kinds of experiments have shown that physiological and experiential factors related to feeding and motivation may modulate the responsiveness of taste neurons in the NST.

5.4.1.1. Feeding and Appetite

Several studies have shown that manipulations that alter an animal’s state of satiety can have an influence on the responses of gustatory neurons in the NST. For example, hyperglycemia produced by intravenous infusion of glucose decreases the sensitivity of NST gustatory neurons to orally applied glucose, with a 43% reduction in sensitivity observed on average across cells [47]. Blood glucose levels had considerably less of an effect on responses to NaCl and HCl (20% and 16% reductions, respectively) and nominal effects on responses to quinine (3%). Accordingly, intravenous infusion of glucose resulted in a decreased preference for glucose by rats but did not affect behavioral responses to quinine [48]. Thus, elevation of blood sugar levels results in decreased gustatory sensitivity to sugars in the NST, accompanied by a reduction in the intake of glucose. Intravenous infusion of other satiety factors, including insulin and pancreatic glucagon, also decreased NST multiunit responses to glucose, although cholecystokinin (CCK) had no effect on taste activity [49]. Similarly, the responsiveness of NST neurons to taste stimulation is also reduced following gastric distension [50]. Such factors could play a part in contributing to satiety and appear to decrease gustatory sensitivity to nutritive/caloric taste stimuli.

The ability to detect and consume sodium is critical for survival, and sodium appetite has been shown to modulate the responsiveness of gustatory neurons. Under normal conditions, rats will choose to ingest NaCl at isotonic concentrations. Thus, 0.17 M NaCl is avidly consumed, whereas other concentrations are less preferred or avoided, and rats can voluntarily mix NaCl and water to maintain isotonic levels of intake [51]. However, when plasma sodium concentration declines, the creation of a specific sodium appetite may serve to return sodium levels to baseline levels [52], and taste sensitivity to salts could play an important role in this drive. Rats that are made sodium deficient will display strong preference for sodium salts, such as NaCl [53,54], and will ingest these salts at concentrations that might otherwise be avoided. Under conditions of sodium deficiency, the sensitivity to NaCl in gustatory neurons in both the chorda tympani [55,56] and NST [57] is decreased. This reduction is particularly apparent in those neurons most responsive to sodium salts. These results have been interpreted to mean that a sodium-deprived rat would require greater ingestion of NaCl to establish the same level of sensory input as a normal rat [56]. Thus, during sodium deficiency, decreased gustatory responsiveness is associated with increased intake of NaCl.

5.4.1.2. Conditioned Avoidance

Conditioned taste aversion is an associative learning phenomenon first described by Garcia and colleagues [58], by which a normally appetitive taste stimulus can be made aversive to a rodent by pairing this stimulus with gastrointestinal illness. Rodents will then associate the resulting illness with the taste of the conditioned stimulus (CS) and will avoid it and other stimuli with a taste similar to the CS [59,60]. This behavior has survival value for the animal by avoiding the ingestion of food that contains toxic substances. Thus, such learning would be expected to be reflected in the activities of gustatory neurons. Accordingly, Chang and Scott [61] demonstrated that aversive conditioning to saccharin increased the responsiveness to the CS in those NST neurons most responsive to sweet substances. Thus, such conditioning alters the responsiveness of gustatory neurons generally by making the CS a somewhat more effective stimulus, possibly serving the function of amplifying the neural message for the CS so that it can be reliably detected and readily avoided.

5.4.2. Electrophysiological Analysis of Descending Modulation

Taste information is projected from the NST to the PBN, which, in turn, sends information to two different systems: a thalamocortical pathway and limbic forebrain structures. Anatomical experiments have shown that some PBN neurons project to the ventral posterior medial nucleus parvicellularis (VPMpc) of the thalamus, whereas another subset of neurons projects to the central nucleus of the amygdala (CeA) [62]. From the VPMpc, taste information is transmitted to the gustatory insular cortex. PBN neurons also project to the lateral hypothalamus (LH), bed nucleus of the stria terminalis (BST), and the substantia innominata [63,64]. Structures in the limbic forebrain gustatory pathway have been implicated in food and water regulation, sodium appetite, taste aversion learning, and the response to stress, suggesting a substrate through which taste activity interfaces with motivated behavior.

There are direct descending projections from the gustatory cortex, the lateral hypothalamus, the central nucleus of the amygdala, and the bed nucleus of the stria terminalis to the gustatory regions of the NST (Figure 5.3) [65–68]. All of these forebrain regions of the gustatory system have been shown to produce modulation of taste activity in the NST. Thus, the responsiveness of brainstem gustatory neurons reflects not only sensory input from the taste receptors, but also descending influences from forebrain circuits. Such descending influences may constitute a neural substrate that contributes to the modulation of NST activity under altered physiological and motivational states.

FIGURE 5.3

The responsiveness of NST gustatory neurons reflects not only sensory input from the taste receptors, but descending influences from forebrain circuits. Gustatory circuits in the rNST receive sensory input from cranial nerves VII, IX, and X. In rodents, (more...)

5.4.2.1. Cortex

Di Lorenzo and Monroe [69] showed both increases and decreases in the responses of NST neurons to taste stimulation following blockade of activity in the gustatory cortex (GC) by local infusion of procaine, revealing that NST cells can be either facilitated or inhibited by cortical influences. Investigators employing electrical or chemical stimulation of the GC in hamsters have arrived at similar conclusions [45]. The results of a recent study showed that of 50 neurons recorded from the hamster NST, 17 (34%) were modulated by ipsilateral GC stimulation. About half of these (8/17) were inhibited, and half (9/17) were excited. Although the excitatory effects were distributed across S-, N-, and citric acid-best neurons, the inhibitory effects of cortical stimulation were significantly more common in N-best NST cells. A more recent experiment, in which stimulating electrodes were implanted bilaterally in the GC, revealed a tendency for greater modulatory influence of the contralateral cortex on NST neurons: 16 of 50 cells (32%) were found to be modulated ipsilaterally, whereas 20 of 50 (40%) responded to contralateral stimulation [70]. Further, 11 of these cells received converging modulation from both sides of the cortex. Thus, among the 50 cells recorded, 25 (50%) were modulated by one or both sides of the GC. These stimulation experiments and earlier studies in which the cortex was anesthetized with procaine demonstrate that gustatory processing in the NST is directly modulated by activity in the GC.

5.4.2.2. Lateral Hypothalamus

Neurons within the lateral hypothalamus are known to alter their activities during food ingestion [73,74] and respond to taste stimuli applied to the oral cavity [71,72]. There are descending projections from the LH to the NST [67,68], and prior work has suggested that stimulation of the LH enhances the responsiveness of rat NST neurons to chorda tympani nerve stimulation [75] and electrical stimulation of the tongue [76]. A recent study in hamsters showed that electrical and chemical stimulation of the LH resulted in orthodromic modulation of half (49/99) of NST taste-responsive neurons [77]. The effects were predominantly excitatory, with 44 of 99 cells showing response facilitation, whereas only 6/99 cells were inhibited. Thus, LH modulation of taste-responsive cells of the NST primarily results in facilitation in responding in these cells. Given that stimulation of the LH induces feeding behavior and lesions of the LH reduce food intake [78,79], and that these effects have been shown to interact with taste-guided behavior [80–82], it is possible that activity in the LH could serve the function of enhancing the gain of NST taste neurons during bouts of feeding [77]. This could serve as a mechanism to increase tastant detectability.

5.4.2.3. Central Nucleus of the Amygdala

The central nucleus of the amygdala contains neurons that respond differentially to hedonically positive and negative taste stimuli [83], and both the CeA and basolateral amygdala are involved in conditioned taste-aversion learning [84]. There is a direct descending projection from the CeA to the NST [64,66,85], and stimulation of the CeA has been shown to alter taste processing in the rNST. A study in hamsters showed that electrical activation of the CeA orthodromically modulated 36 of 109 taste-responsive NST neurons (33%) [86], 33 of these cells were excited, and 3 were inhibited. Interestingly, those neurons that were modulated by the CeA displayed significantly smaller responses to taste stimuli than those that were not modulated by the CeA. However, there was no specific effect on any one stimulus or cell type within the NST.

Moreover, a recent investigation has shown gustatory neurons in the NST that are modulated by the CeA are often also modulated by additional forebrain structures [87]. Of 113 cells in the hamster NST that were modulated by stimulation of either the LH or the CeA, 52 of them were responsive to stimulation of both of these sites. In this study, the influence of either site was most often similar, although there were cells that were excited by one site and inhibited by another. It becomes clear from these data that there exists a complex descending influence on the gustatory responses of cells in the rNST.

5.4.2.4. Bed Nucleus of the Stria Terminalis

The dorsolateral bed nucleus of the stria terminalis sends a descending projection to the NST [67,68,88]. A recent study in hamsters revealed that stimulation of the BST resulted in inhibition of responding in 29/101 NST taste neurons, whereas only 7 were excited. All types of NST neurons were found to be affected by BST stimulation. However, the number of NaCl-best neurons was fewer and citric acid-best cells greater among those modulated by the BST than expected by chance. Although various subnuclei of the BST are implicated in a number of neural systems, including those involved in responses to stress [89,90], and in motivation, reward, and drug addiction [91,92], the role played by the BST in the processing of taste information is not clear. It is possible that descending input from the BST could play a role in the effects of stress on eating behavior [93–95].

Overall, experiments described here have revealed an extensive centrifugal modulation of gustatory responsiveness in the rNST. Essentially every forebrain target of the gustatory system, including the GC, LH, CeA, and BST, exerts a certain degree of influence on the taste response properties of NST neurons. In summary, the LH and CeA exert a predominantly excitatory effect on NST taste cells, whereas the GC and particularly the BST produce significant inhibition. This extensive neural substrate no doubt underlies the modulation of taste activity by physiological and experiential factors. Future studies geared towards understanding how these pathways are engaged by alterations in blood glucose, gastric distension, conditioning, and other physiological conditions known to alter taste activity may provide further clues as to the exact function served by descending input from these structures on taste processing in the NST.

5.5. THEORIES OF TASTE QUALITY CODING

How is information about taste quality carried by the activities of gustatory neurons? The issue of gustatory quality coding has been the subject of a longstanding debate, which has largely revolved around two coding models: the labeled-line and across-neuron pattern theories. Both of these are spatial models of neural coding: Labeled-line theory postulates that information about taste quality is signalled by which cells are active, whereas across-neuron pattern theory declares that a pattern of activity across a group of cells carries information about stimulus quality. Here, we will briefly review these theories along with their basic assumptions. In Section 5.6, we will discuss evidence regarding a spatial neural code for taste, focusing on the relationship between recent developments on the molecular biology of taste receptors and the response properties of gustatory neurons in the rNST. Further, we consider how the time course of spike activity could play a role in taste coding in the rNST.

5.5.1. Across-Neuron Pattern Theory

Early neurophysiological studies revealed that gustatory neurons in several species are sensitive to stimuli representing more than one taste quality. These data led to the idea that taste quality information is encoded by relative patterns of activity generated across a population of neurons [96,97]. This theory became known as coding by across-neuron patterns in the study of gustation. Under this model, the activity of any one cell is only meaningful when taken in the context of its neighbors [96–99]. The common conceptualization of pattern theory in gustation has been largely based on the degree of pairwise correlation between activity profiles produced by different tastants across a large number of gustatory neurons. Under this framework, discriminability between two stimuli is inversely proportional to the degree of correlation between their patterns produced across a population of cells [100]. The degree of correlation between across-neuron patterns of response does, in many cases, appear to reflect the degree of perceived similarity between taste stimuli. Stimuli that taste similar to humans and are categorized as similar by rodents in behavioral experiments evoke correlated patterns of response, whereas dissimilar stimuli produce uncorrelated across-neuron patterns [98,99].

Across-neuron pattern theory accommodates the multiple sensitivities of taste cells and proposes that individual neurons contribute to the representation of more than one taste quality. This model assumes that a downstream processor of gustatory neurons “knows” the pattern associated with each stimulus. Only then can the pattern of activity across gustatory neurons convey meaning [98]. In its purest form, across-neuron pattern theory places no importance on the existence of neuron types. However, some studies have shown that the activities of certain types of neurons are critical for establishing different patterns of response between tastants. For example, St. John and Smith [18] used multivariate procedures to demonstrate that the activities of amiloride-sensitive N-best neurons in the rNST are critical to establish distinguishable across-neuron patterns of response to NaCl and KCl, stimuli that are behaviorally discriminable by rodents [40].

5.5.2. Labeled-Line Theory

In lieu of across-neuron pattern theory, some have argued that a particular stimulus quality is encoded by the activation of one of a few discrete types of gustatory neurons. In this theory of coding, known as labeled-line, cells with a common best-stimulus are purportedly dedicated to represent the qualitative features of only this stimulus [101,102]. Labeled-line theory requires that activity within a given type of neuron is both necessary and sufficient to represent a single stimulus quality. Under this framework, a hypothetical decoder of gustatory neurons would use a binary “on/off” strategy to read out stimulus quality: When a group of neurons with a common best-stimulus is active, the decoder would report the quality of this stimulus, otherwise a stimulus of this quality is not present. Thus, sweetness, for example, is assumed to be encoded by activity in exclusively sweet-best (e.g., sucrose-best) neurons. Labeled-line theory has been largely argued to be supported by data showing that the activities of purported functional groups of neurons correlate with behavioral responding to these stimuli [102,103].

5.6. TASTE CODING IN THE rNST

Although there has been considerable progress in recent years in our understanding of both the central neural processing and the receptor and transduction mechanisms for taste, investigators from these two perspectives view coding in the gustatory system very differently. Recent advances in molecular biology have reinvigorated the idea that taste quality may be coded by hard-wired labeled lines, which faithfully represent stimulus quality [104–106]. On the other hand, many investigators who record the activity of the broadly tuned neurons of the central nervous system favor a population code for taste quality [3,107]. As a consequence, there is still considerable controversy and debate over whether taste information is represented by specific neuron types or by the ensemble of activity across a population of broadly tuned neurons. Here, we focus on understanding the relationship between taste receptors and the response properties of neurons in the rNST.

5.6.1. Specificity of Input from Taste Receptors?

A family of genes has been discovered in mammals that accounts for much of the behavioral sensitivity towards stimuli characterized by humans as tasting sweet or savory (umami). These genes, the Tas1rs, encode functional G protein-coupled receptors (GPCRs) that have been demonstrated in vitro to bind sweet stimuli such as sugars, artificial sweeteners, and sweet proteins, or umami stimuli such as L-amino acids [108–114]. A second class of taste receptors, the T2rs, function as receptors for bitter-tasting compounds [8,115]. To date, approximately 25 human and 33 mouse functional bitter taste receptor genes (Tas2rs) have been identified [116]. T2rs are expressed in taste receptor cells throughout the oral cavity; individual cells tend to express multiple T2rs [8].

Expression studies have suggested that the genes for the T1r and T2r receptors, which are involved in responses to sweet and bitter stimuli, respectively, are not co-localized within receptor cells [117]. Further, experiments in which cells normally expressing T1rs are instead made to express a T2r [118] or an opioid receptor [119] show that these taste bud cells are hardwired to signal a sweet (or palatable) taste. These authors and others [120] have suggested, therefore, that taste quality is represented by different cells in a labeled-line fashion. However, it has been known for some time that sweet and bitter stimuli are predominantly coded by different cells in the gustatory pathway [121]. Recent molecular data also suggest segregation between sweet and bitter taste in the CNS [122], largely reflecting differences in the sensitivity of the VIIth and IXth nerves, which have a somewhat segregated central distribution.

A labeled-line code for taste would require that the purported tuning specificity observed at the level of the receptor cell be recapitulated at all levels of the nervous system. It has generally been observed that central gustatory neurons are differentially sensitive to sweet or bitter stimuli [121], which agrees with the nonoverlapping expression patterns of receptors for these tastants. However, central sweet- or bitter-sensitive gustatory neurons including those in the rNST respond also to salts and acids [9,123], potentially making the response of any one cell class equivocal with respect to taste quality [3]. Thus, even though different cells respond to sweet and bitter stimuli, this by itself is not definitive evidence that these cells comprise labeled lines for coding sweetness and bitterness. Moreover, an argument for a labeled-line code based on the observation that dedicated behavioral responses arise following the activation of types of taste receptor cells [124] must be cautiously evaluated, given that this phenomenon could be effectively argued to occur independently of the mechanism of gustatory coding. Although the stimulation of taste bud cells that express sweet receptors, for example, will undoubtedly result in the transmission of a “sweet” message to the brain, the perception of sweetness will follow regardless of whether this message is encoded by a labeled-line, pattern of activity, or other mechanism in the CNS (Figure 5.4). Studies of taste receptors themselves cannot address the organization of downstream central circuits that process input from these receptors, which more critically define the logic of gustatory neural coding.

FIGURE 5.4

Dedicated behaviors that arise following stimulation of specific kinds of taste receptors do not necessarily reflect that gustatory circuits are organized according to a particular neural coding strategy. Here we hypothetically show this using the example (more...)

It is worth mentioning that functional studies of taste bud cells in intact tongue and palate epithelia have shown these cells to be slightly more narrowly tuned on average than CT nerve fibers [125], but not as specific as the molecular data on GPCRs would suggest. For example, calcium imaging and electrophysiological studies have shown that a proportion of mammalian taste bud cells responds to bitter stimuli and those of other taste qualities [125–127]. Moreover, calcium imaging studies in Drosophila have shown that taste receptor cells that respond to palatable sweet stimuli, such as sucrose or glucose, respond just as well to stimuli that are aversive to the fly, such as KCl or high concentrations of NaCl [128]. In this study, it was postulated that these appetitive and aversive stimuli are detected through a comparison of inputs from different kinds of taste cells. Although not argued in this paper, this hypothesis is in line with a pattern code for taste.

5.6.2. Responsiveness of rNST Neurons

Cells at all levels of the gustatory system, including taste bud cells [129–131], exhibit multiple sensitivities to stimuli representing different taste qualities to humans and different behavioral categories to rodents [32,132,133]. In the periphery, sucrose-best fibers in the CT nerve [32] and QHCl-best fibers in the IXth nerve [29,30] are relatively narrowly tuned, largely reflecting the segregation of T1r and T2r receptors in the taste buds. However, due to convergence of peripheral fibers onto central neurons [5,7,16], cells in the CNS are more broadly tuned [14,121]. Thus, whatever specificity exists at the level of the taste bud cell is degraded as distal elements converge onto more proximal ones.

In the rNST, the broad tuning of gustatory neurons in this structure questions whether individual cells or purported functional groups of them could reliably signal only a single stimulus quality [98,99]. For example, rat NST neurons that respond optimally to HCl or quinine are not differentially sensitive to these stimuli relative to Na⁺ salts [9]. If neurons of this class exclusively encode an aversive/bitter taste when activated, it would be expected that NaCl would elicit a distinctly bitter taste, given that NaCl drives these acidic/bitter-sensitive neurons just as effectively as many strongly bitter stimuli [9]. Furthermore, under this type of coding strategy, NaCl would be expected to possess a sweet-taste component given that sucrose-best NST neurons receive significant input from amiloride-sensitive sodium receptors [17,18,42] that mediate the detection of sodium taste [40]. However, rodents categorize NaCl as perceptually independent of sweet, acidic, or bitter stimuli in behavioral paradigms [103,134].

If identified neuron types in the rNST are coding stimuli in a labeled-line fashion, their broad tuning would suggest that they would do so very poorly. The broad tuning of gustatory neurons in the rNST is more conducive to a population-based model of coding [3,98,99], where information about taste quality is carried by the activity of an ensemble of heterogeneous gustatory neurons. Information about taste quality may be contributed by the absolute pattern of response across neurons or relative levels of activation between different kinds of multisensitive cells.

5.6.3. A Temporal Component to Gustatory Coding in the rNST

Temporal coding implies that information about a stimulus is carried by the time course of spike activity. There is evidence from neurophysiological studies in both the gustatory cortex [107,135] and brainstem [136,137] that the time course of spike firing could play a role in the neural code for taste. In a recent study in the rNST, Di Lorenzo and Victor [137] used a theoretical approach to measure how spike timing, spike intervals, and spike counts in the responses of gustatory neurons could provide information about stimulus quality. Here, neurons were stimulated with individual tastants over many trials. It was found that the spike counts of some cells were highly variable from trial to trial, resulting in a changeable best stimulus in some cases. In these cells, spike count would provide equivocal information about stimulus quality. Yet spike timing was found to improve stimulus discriminability in many neurons. Interestingly, cells with the most variable spike counts tended to show the largest improvements in stimulus discriminability when temporal parameters of responses were considered.

Could both temporal and spatial parameters of neuronal responses contribute to gustatory coding? Although the timing of spikes would carry no meaning under a labeled-line strategy, it is conceivable that temporal coding could very well function in parallel with a population-based code for taste. This arrangement could provide a mechanism to increase the information-handling capacity of a group of cells, with temporal parameters providing an additional dimension to, for example, carry information that might allow for fine discriminations between stimuli. Whether the brain indeed adopts algorithms to read out gustatory information based on these parameters remains an open question.

ACKNOWLEDGMENT

The authors would like to acknowledge the support of NIH grants DC00353 and DC008194 from the National Institute of Deafness and Other Communication Disorders in the preparation of this chapter.

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