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1Department of Psychology, Binghamton University, Binghamton; and 2Department of Neurology and Neuroscience, Weill Medical College of Cornell University, Ithaca, New York
Submitted 16 August 2007; accepted in final form 29 September 2007
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ABSTRACT |
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INTRODUCTION |
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, 2006) and gustatory cortex (Katz et al. 2001, 2002). In the nucleus of the solitary tract (NTS, the first synaptic relay in the central gustatory pathway), spike timing within the first 2 s of a taste response was found to convey a significant amount of information about taste quality in about half of the cells tested (10 of 19, 53%; Di Lorenzo and Victor 2003). Stimuli that were used in that study each represented a separate taste quality and were thus at the limits of the perceptual domain of the system. That is, these stimuli were very distinct from one another. Although taste stimuli are more diverse in the natural world, they can nevertheless be categorized into groups arranged according to taste qualities (Nowlis et al. 1980). Within these categories, taste stimuli of the same taste quality are more similar to each other than they are to taste stimuli of different qualities. Although we have demonstrated that temporal coding can be used to signal differences among tastants of different qualities, the extent to which temporal coding can also be used to signal both the similarity of tastants of the same taste quality (but different chemical composition, e.g., NaCl and LiCl, both salty) and the differences between them is as yet unclear.
In studies of neural coding in the gustatory system spatial theories such as labeled line (e.g., Frank 1973, 2000; Lundy and Contreras 1999; Scott and Giza 1990) and across neuron pattern (ANP; e.g., Doetsch and Erickson 1970; Ganchrow and Erickson 1970; Yamamoto and Yuyama 1987) theories have dominated the literature. These theories are based on the relative "response magnitudes" to a variety of tastants that represent the basic taste qualities (sweet, sour, salty, bitter, and umami). This measure is the number of spikes evoked by a given tastant over an interval of time that includes the stimulus presentation (for a discussion see Di Lorenzo and Lemon 2001). The labeled line theory assumes that each taste-responsive cell encodes stimuli of a single taste quality, typically defined by its "best" or most effective stimulus (reviewed by Spector and Travers 2005). In the ANP theory, the pattern of response magnitudes across the entire population is thought to convey the identity of a stimulus. Responses across cells to tastants that are of similar quality are more highly correlated than responses to dissimilar tastants. Both theories assume that taste-evoked spikes are integrated over the response interval and that the temporal arrangement of spikes within that interval is irrelevant; however, these assumptions may not be true (Di Lorenzo and Victor 2003, 2007; Hallock and Di Lorenzo 2006; Katz et al. 2002).
Use of the response magnitude as a starting point for spatial theories of taste coding is based on the premise that this measure is a reliable index of a cell's sensitivity to a taste stimulus. Studies directed at examining response variability, however, have challenged this expectation. For example, Ogawa et al. (1973, 1974) showed that responses in some chorda tympani fibers (CT, a branch of the VIIth nerve innervating taste buds on the rostral two thirds of the tongue) can vary widely when taste stimulus presentations are repeated. In the NTS of the rat, taste responses in some cells can vary with repetition to such an extent that the best stimulus and the breadth of tuning can change (Di Lorenzo and Victor 2003). Although there are several variables that have been shown to change the best stimulus of taste-responsive NTS cells (Chang and Scott 1984; Di Lorenzo and Monroe 1995; Di Lorenzo et al. 2003; Jacobs et al. 1988; Smith and Li 2000), variability on a trial-by-trial basis begs the question of how these cells convey an unambiguous message about a taste stimulus on the tongue.
The purpose of the present study was to study the neural coding of tastants of similar quality but different chemical composition in taste-responsive NTS cells in the rat. We first examined average firing rate and how it varied across repeated stimulus presentations. Our hypothesis was that responses to stimuli of similar quality would evoke responses whose firing rates were more tightly clustered than the distribution of firing rates elicited by stimuli of different taste qualities. In particular, we hypothesized that firing rate could be used to discriminate among different taste qualities (e.g., salty and bitter), but not between stimuli of similar taste qualities (e.g., NaCl and LiCl, both salty). The second set of analyses was aimed at quantifying the extent to which temporal coding could be used to distinguish between tastants of similar qualities as well as to differentiate among tastants of different taste qualities. Based on our previous studies (Di Lorenzo and Victor 2003, 2007), our hypothesis was that temporal coding would be used to discriminate among taste stimuli when differences in the number of spikes evoked by each of the tastants was insufficient to identify the tastant. Further, based also on these studies, we predicted that cells with the most variable responses would be more likely to exhibit temporal coding. Because past work has shown that temporal coding was most prominent in the initial portion of the response, we focused our analyses on the first 2 s of the taste response.
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METHODS |
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Thirty-eight male Sprague–Dawley rats (350–450 g) were used in this study. Rats were pair housed with a 12-h light–dark schedule (lights on at 7:00 am) and given unrestricted access to fresh chow and water. A tasteless plastic tube was placed in the cage for environmental stimulation. Care was maintained according to the requirements of the Institutional Care and Use Committee of Binghamton University.
Surgery
Before surgery, rats were anesthetized with urethane [1.4 g/kg, administered intraperitoneally (ip) in two doses, 20 min apart]. Animals received an injection of pentobarbital (Nembutal, 25 mg/kg, ip) 20 min after the second dose of urethane. Supplementary injections of urethane were administered as needed to maintain anesthesia. Glycopyrrolate, a peripheral anticholinergic agent, was administered to facilitate breathing when necessary (Robinul diluted to 10% in isotonic saline, 0.0004 g/kg, administered subcutaneously). Temperature was maintained between 35 and 37°F with an anal thermistor probe connected to a heating pad (FHC).
Animals were tracheotomized and placed in ear bars and the head was held in place 5 mm below the interaural line. The skin and fascia were removed to expose the skull and a nontraumatic head holder was attached to the skull using stainless steel screws and dental cement. The occipital bone and underlying meninges were removed and the posterior cerebellum was gently aspirated to reveal the floor of the fourth ventricle.
Stimuli
Four pairs of taste stimuli were used in this experiment, each stimulus of a pair predominantly evoking the same taste quality. Stimuli were reagent-grade chemicals dissolved in distilled water and present at room temperature. Pairs of stimuli were as follows: 0.1 M NaCl and 0.1 M LiCl (salty), 0.01 M HCl and 0.01 M citric acid (sour), 0.01 M quinine HCl and 1.0 M urea (bitter), and 0.5 M sucrose and 0.3 M fructose (sweet). Concentrations of the stimuli that were both exemplars of a given taste quality evoked similar average response magnitudes across cells. Figure 1 shows the average response magnitudes evoked by each stimulus used in the present study.
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Taste stimuli were bathed over the tongue through a specially designed stimulus delivery system. A mouthpiece, consisting of six stainless steel tubes (1-mm diameter) with holes along the entire length top and bottom, was positioned in the mouth so that the holes were facing the tongue surface and palate. These tubes were connected to pressurized (compressed air, 
10 lb./in.2) reservoirs of tastants with polyethylene tubing through an array of computer-controlled solenoid valves. Each tube was associated with its own fluid reservoir, i.e., taste stimulus or distilled water. When a solenoid valve was actuated, fluid was sprayed over the entire mouth, including the nasoincisor ducts. Fast green dye was delivered to one animal, and its tongue was removed and inspected with an operating microscope to ensure that fluid was delivered to the fungiform, foliate, and circumvallate papillae. When the solenoid closed, flow of stimulus stopped without dripping due to the surface tension of the liquid in the tubes. Standard flow rate through this system was 5 ml/s, regulated by a pinch valve positioned on the tube leading from the reservoir to the mouth. Flow rates for all stimuli were calibrated daily before each experiment.
After surgery, the ear bars were removed, the animal's mouth was opened, and a small weight was attached to the tip of the tongue so that the tongue was extended. The mouthpiece, as described earlier, was then inserted into the animal's mouth.
Recording and testing
An etched tungsten microelectrode (18–20 M
, 1 V at 1 kHz, FHC) was slowly lowered into the brain stem above the rostral portion of the NTS, at 2.7 mm anterior and 1.8 mm lateral to the obex. The signal was amplified (Model P511, Grass Technologies) and monitored with a speaker and an oscilloscope. To locate the taste-responsive portion of the NTS, a solution of 0.1 M NaCl was periodically bathed over the tongue, followed by a distilled water rinse.
Single taste-responsive cells were isolated based on the consistencies of their waveform (see following text). A cell's response profile was recorded as its response to presentations of exemplars of the four basic taste qualities. Stimuli were presented in the following order: NaCl, HCl, sucrose, and quinine. Each taste trial consisted of a 10-s distilled water prerinse, 5-s stimulus presentation, 5-s pause, and a 20-s distilled water rinse. Each stimulus delivery occurred 
2 min after the onset of delivery of the previous stimulus.
Two of the four tastants that were tested initially were selected for further study, based on the responsiveness of the cell. The tastants selected were usually, but not always, the stimuli that evoked the two most vigorous responses during the initial response profile. In some cases, selection of a "third-best" stimulus for further study rather than a "second-best" stimulus was made to ensure a representative sample of responsivity in our data set. Given the variability in response magnitudes with repeated stimulus presentations that we have observed (Di Lorenzo and Victor 2003, 2007), the distinction between second-best and third-best was not always obvious and may not be meaningful. For all cells, two tastants were selected from among stimuli representing one of four taste qualities, along with two paired tastants evoking similar qualities to those selected. For example, if NaCl were selected, LiCl, another salty stimulus, would also be tested. Likewise, if HCl were selected, citric acid, another sour tastant, would also be tested. Tastants of similar quality were never presented consecutively.
Histology
At the end of the recording session, a lesion was made by passing DC current (1 mA cathodal, 5 s) through the recording electrode. Brains were removed and placed in formalin for a minimum of 2 wk. To verify the location of the lesion in the rostral NTS, the brain stems were cut into 40-µm sections and stained with cresyl violet.
Data analysis
Electrophysiological responses were digitized with an analogue-to-digital interface (Model 1401, Cambridge Electronic Designs) and recorded on a computer. Waveforms associated with single cells were identified with template matching and principal component analysis using Spike2 software (Cambridge Electronic Designs). The precise timing of each spike (1-ms resolution) was recorded with respect to the onset of each stimulus delivery (as defined by the time of solenoid activation). A taste response was defined as a cell's average firing rate over the 2 s of stimulus delivery minus its average firing rate during the 5 s of water delivery immediately preceding stimulus onset. A response was considered significant when the firing rate during the first 2 s of stimulus presentation was 50% greater than the firing rate during the preceding 5 s of prerinse water presentation. Responses were expressed in spikes/s (sps).
Variability in a cell's responses to a given tastant was assessed by calculating the coefficient of variation (CV, SD/mean). Pearson's r was calculated for each cell's responses to all pairs of tastants, similar and dissimilar.
Analysis of temporal patterns of response
To characterize the contribution of the temporal structure of a response to the neural code for taste, spike trains were analyzed by the metric-space method of Victor and Purpura (1996, 1997). These analytical methods provide a rigorous way to determine whether the statistics of the precise times of individual spikes, or of the pattern of interspike intervals, have the potential to carry information concerning taste quality. This analysis derives a family of metrics that measure "distance" (i.e., dissimilarity) between spike trains. Each of these metrics represents the "cost" of transforming one spike train into another by changing a different aspect of the spike trains that were compared. These included the number of spikes and the precise timing of spikes. The simplest of this family of metrics represents the difference in the number of spikes contained in two spike trains associated with two responses. To calculate cost in this case, each spike that is either deleted or added incurred a cost of "1," so that this metric, called Dcount, is simply the arithmetic difference between the number of spikes contained in each response.
To measure the difference between two spike trains in terms of the arrangement of spikes in time requires a definition of how close in time two spikes need to occur to be considered "equivalent." In the family of metrics described by Victor and Purpura (1996, 1997), the similarity of the timing of spikes, or the sequence of interspike intervals, in two responses is calculated at a variety of levels of precision, measured by a parameter called "q." The cost of adding or deleting a spike is set at "1" as in Dcount and, in addition, the cost of moving a spike (or interspike interval) by an amount of time t is set at qt, where q is in units of 1/s. The resulting metric for spike timing is called Dspike[q].
Information (H) is calculated from these distances by determining the extent to which responses to each stimulus form distinct clusters (see Victor and Purpura 1996, 1997 for details). Information is determined independently for a range (0 to 500) of values of q. The value of q at which information is maximized is denoted qmax, and the maximum value of H is denoted Hmax. In the present experiment, the data were analyzed as though each stimulus evoked a separate taste quality, even though the data set contained responses from two sets of tastants of similar quality. Thus the maximum possible value of H (in bits) for discrimination of any pair of tastants was 1 (log2 2 = 1). The relative contributions of spike count and spike timing to the information conveyed by taste responses were thus quantified by Hcount (obtained from Dcount, i.e., q = 0) and Hmax (obtained from Dspike[q] at q = qmax).
For some cells, the amount of information conveyed by spike count (Hcount, at q = 0) was at least as large as the amount of information conveyed by the spike timing, (Dspike[q]) at all other values of q (i.e., Hcount Hmax). For these cells, spike count was said to provide all the information available to distinguish between these stimuli. Alternatively, the rate envelope and/or spike timing pattern contributed to information when Hmax > Hcount. We used Hres, the amount of information conveyed by responses with randomized spike timing but the same rate envelope as the real responses, to distinguish between information contained in the rate envelope and information present in the precise timing of spikes; if Hmax > Hres + 2SD and Hmax > Hcount, spike timing significantly contributed information to the taste responses above that contributed by spike count or the rate envelope. The rate envelope was said to provide information for a discrimination when Hmax 
Hres + 2SD and Hmax > Hcount. The value of q at which Hmax was observed indicated the temporal precision with which spike timing was most significant. An additional control analysis was conducted in which the labels of each response were randomized and the information recalculated, called Hshuff. In the present data set, these values were uniformly smaller that the information conveyed by the actual responses.
Importantly, two additional analyses served as controls for the possibility of spurious results; these are detailed in Victor and Purpura (1996, 1997). Briefly, in the first of these, called "shuffled," values of H calculated as described earlier were compared with values H0 obtained from 10 to 40 surrogate data sets in which the tastants associated with each response were randomly scrambled. This control is necessary because estimates of H have an upward bias, which is conservatively estimated by H0. Only values of H that exceed the range (mean ± 2SD) of values of H0 were considered to represent better-than-chance classification. In a second analysis, known as "exchange resampling" (Victor and Purpura 1996), surrogate data sets were created by resampling the original data so that responses to each tastant matched the poststimulus time histograms of the observed responses and individual responses also had the same number of spikes, although spike train patterns were destroyed. We then compared values of H obtained from the real data with values Hres obtained from the same analysis on 10 to 40 of these resampled data sets. If H was above the range (mean ± 2SD) of values of Hres, we concluded that the observed temporal coding is not merely due to the average temporal profile of the response to each tastant (with the overall variability in spike count taken into consideration) and that the arrangement of spikes in time in individual trials must play a role in conveying information.
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RESULTS |
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Responses to between 3 and 21 presentations (median = 11) of each of four taste stimuli (two exemplars of each of two taste qualities) were recorded from 38 cells. Across all cells the average spontaneous rate was 3.2 ± 0.7 sps. Among the 35 cells that had the initial response profiles available, 20 were identified as NaCl best, 5 as HCl best, 4 as sucrose best, and 6 as quinine best. Complete response profiles are not available for 3 cells because of experimenter error. One cell responded equally well to NaCl and sucrose in the initial response profile; this cell was classified as NaCl best because it responded better to salts than to sugars on all subsequent trials.
Recording sites were determined for 20 cells. Seven of these (35%) were located within the intermediate subdivision of the NTS, 7 (35%) within the lateral subdivision, 2 (10%) within the medial subdivision, 3 (15%) within the dorsomedial subdivision, and 1 (5%) in the dorsolateral subdivision.
Response magnitude varied across trials
Several observations attest to the variability of response magnitude with stimulus repetition. For example, although most cells showed repeated significant responses throughout the recording session to the stimuli to which they responded initially, 16 cells (of 38; 42%) failed to respond to a given stimulus on at least one trial (Table 1). In addition, across repeated blocks of four trials (consisting of two pairs of tastants of similar qualities) both the order of effectiveness of stimuli of the same quality and that of different qualities changed in the majority of cells. These changes were found more frequently for responses to tastants of the same quality (33 cells with a median of four changes in order of effectiveness) compared with responses to tastants of different qualities (15 cells with a median of two changes in order of effectiveness). Figure 2 shows examples of both effects in two cells.
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Table 4). Table 3 A shows that responses to both similar and dissimilar stimuli did not covary over time; within-cell correlations therefore provide no indication that as a group they detect similarity between the two salty and the two sour tastants, as might be indicated by larger interstimulus correlations between similar-tasting stimuli. It should be noted, however, that the interval between stimulus presentations was 2 min for dissimilar stimuli and 4 min for similar stimuli; fluctuations over shorter periods of time could not be detected with our experimental design. Median correlations across tastants were also quite low. Across cells, correlations between average responses (across trials) to similar tastants were greater than correlations between dissimilar tastants, although all interstimulus correlations were high, as seen in Table 3B.
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Temporal coding of tastants of similar and dissimilar quality
To analyze the contribution of temporal coding to the neural code for tastants of similar and dissimilar qualities, we used metric-space analyses, as described in METHODS. These procedures allowed quantification of the amount of information that was contributed by spike count alone, by the rate envelope (time course of the response), or by spike timing. Naturally, all three of these mechanisms might, and usually were, observed in any given comparison of responses to tastants. In addition, it is important to note that the maximum information that can be conveyed in any pairwise discrimination is 1 bit. In the present data only a few cells that used spike count alone achieved that value, although some cells for which spike timing and/or rate envelope contributed information came close. Of 40 stimulus–stimulus comparisons where Hmax = 1.0, 32 (80%) were between stimuli of different taste qualities and 30 (75%) also showed Hcount = 1. Figures 4 and 5 show examples of the results of analyses of temporal coding in two cells.
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Analyses of information contributed by temporal coding showed that comparisons of tastants of similar qualities were encoded differently than those for tastants of dissimilar qualities. As shown in Table 4, the amount of information contributed by spike count alone was significantly less for tastants of similar quality than that for tastants of dissimilar quality (Student's t-test, P < 0.001). This was not surprising given the observation that the average difference between response magnitudes was higher for pairs of tastants of dissimilar qualities than for pairs of tastants of similar qualities (see Table 3). Moreover, the larger the mean absolute difference in response magnitude, the larger the amount of information conveyed by spike count (r = 0.49, P < 0.01).
The amount of information contributed by the temporal characteristics of the response in addition to that contributed by spike count was about the same for all pairwise comparisons. Given the fact that the total amount of information contributed by the temporal characteristics of a response was significantly larger for tastants of dissimilar quality than for those of similar quality (Student's t-test, P < 0.001), the temporal characteristics of responses contributed proportionately more information than spike count for distinguishing among tastants of similar quality than for tastants of dissimilar quality. The amount of information provided by the spike timing was therefore proportionally greater for distinctions between tastants of similar quality.
It is important to distinguish between classifying discriminations as easy versus difficult, and classifying discriminations on the basis of whether the tastants are of similar versus dissimilar qualities. We are operationally defining an "easy" discrimination as one that can be made by spike count and a "difficult" discrimination as one that cannot be made by spike count. As shown in Table 2, on average, the response magnitudes evoked by two tastants of similar quality are similar. For cells where this applies, distinction between these two stimuli would qualify as difficult. However, in some cells, two tastants of different qualities evoke similar response magnitudes. This condition would also qualify as a difficult distinction. So, the question that arises is whether temporal coding is used most often for distinguishing between tastants of similar quality or for difficult discriminations, regardless of taste quality. To answer this question, in Fig. 6, we plotted the information conveyed by spike count (Hcount) versus the additional information contributed by temporal characteristics of the response (Hmax – Hcount) for tastants of similar (filled circles) and dissimilar qualities (open circles). This figure shows that there was a range of coding strategies (the scatter across the entire triangle), with temporal coding contributing more when Hcount was low—i.e., a difficult discrimination. This happens whether the comparison is between tastants of similar or dissimilar quality.
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Data presented in Fig. 7 further support the idea that temporal coding is evident more frequently for comparisons of tastants of similar quality than dissimilar quality. It can be seen, for example, that the comparison between the two salty and the two sour tastants showed the largest proportion of cells with a significant contribution of spike timing (NaCl vs. LiCl, 12 of 24 cells, 50%; HCl vs. citric acid, 11 of 19 cells, 58%) relative to all other pairwise comparisons (32% of the cells). In contrast, fewer cells used spike count to differentiate NaCl versus LiCl (1 of 24 cells, 4%) and HCl versus citric acid (3 of 19 cells, 16%) than were used with the other pairwise comparisons (range of 5 to 10 cells of 19, 26–53%). If the number of cells with responses showing a significant contribution of spike timing and the rate envelope were combined, it was evident that the temporal characteristics of response were more widely available to convey information about NaCl versus LiCl (21 of 24 cells, 88%) and HCl versus citric acid (13 of 19 cells, 68%) than the other comparisons (range of 8 to 10 cells of 19, 42–53%).
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DISCUSSION |
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Several observations suggested that response magnitude was only a moderately reliable measure of a cell's sensitivity to a given tastant. For example, variability in response magnitude with repetition was evidenced by the failure to respond to a normally effective tastant (a tastant that produced a response) and by changes in the order of effectiveness of tastants across blocks of trials. Reversal of the more effective stimulus of a pair of tastants across blocks of trials was observed more frequently when both tastants were of similar quality, and therefore of similar response magnitude (see Table 3). If this variability were due to some underlying generating factor on a long timescale (i.e., on a scale of minutes), then responses to tastants of similar quality might be predicted to vary in parallel, resulting in a high correlation of responses across trials. Contrary to this expectation, interstimulus correlations among salty and sour tastants were quite low (<0.20) for both tastants of similar and dissimilar quality. Of course, there may be some underlying factor that modulates responses on a shorter timescale, say tens of seconds, and that might account for the poor interstimulus correlations of responses to tastants of similar quality. Even so, our data are consistent with the suggestion that the response of an individual cell could potentially distinguish between any two tastants regardless of quality [due to the dissimilarity of their across neuron patterns (ANPs)] but could not necessarily group tastants of similar quality. Similarly low interstimulus correlations for both similar and dissimilar pairs of stimuli imply that trial-to-trial variability is not quality specific. In contrast, interstimulus correlations across cells were lower for comparisons of salty versus sour tastants compared with those for salty versus salty and sour versus sour tastants. Unlike individual cells, the entire population of taste-responsive NTS cells may provide more reliable information about the similarity or dissimilarity of stimuli. In other words, the ANPs of responses to tastants of the same quality are more similar than ANPs of responses to tastants of dissimilar quality. In the ANP theory of taste coding, these differences form the basis for both discrimination of tastants of different qualities and the grouping tastants of similar taste qualities (Doetsch and Erickson 1970; Ganchrow and Erickson 1970; Yamamoto and Yuyama 1987).
In previous work using four tastants, each of which represented a different quality, the amount of variability (as measured by the mean CV of responses within a cell) was a strong predictor of the proportional contribution of temporal coding (r = 0.85; Di Lorenzo and Victor 2003). No such relationship was apparent in the present study for any pairwise comparison. At least two differences between the previous and present studies might account for this difference. First, the fact that we selected cells to a great extent on the basis of their responsiveness to our test stimuli (usually, but not always, avoiding weaker responses) may have produced a sample of cells with less variable responses than a more random sample might have. The observation that more robust responses are less variable than weaker ones is consistent with this possibility. Second, the use of stimuli representing only two taste qualities may also have influenced the CV. Previous results showed that the CVs of responses to NaCl, HCl, sucrose, and quinine were not significantly different from each other, although the CVs for sucrose and quinine were larger than those for NaCl and HCl. In the present study, we also found no significant differences in variability across tastants, even when tastants were grouped according to quality. However, because most of our analyses were based on responses to salty and sour tastants, it may be that the CVs produced by these stimuli would underestimate the CV of the cell had it been tested with an array of tastants more representative of the entire perceptual domain.
Although response variability was not a good predictor of the relative contribution of temporal coding, we did find that the most difficult distinctions—i.e., between tastants of similar quality and/or similar response magnitude—showed evidence of temporal coding more frequently and to a proportionately greater degree. Conversely, comparisons of tastants of dissimilar qualities were found to rely more on spike count than the temporal characteristics of the response. Perfect discrimination between any two tastants, even two tastants of the same quality, requires 1 bit of information. Accordingly, distinguishing between two tastants of the same quality would require that same amount of information as distinguishing between two tastants of different qualities, even though it is intuitively more difficult to distinguish between two similar tastants than two dissimilar tastants. In the present study, the only pairings for which taste responses conveyed 1 bit of information were those in which spike count sufficed (see Fig. 6), and these were mostly pairings of tastants of dissimilar qualities. Conversely, in the more difficult distinctions (including all of those between two tastants of similar quality), the information is <1 bit, indicating that distinction based on spike count and timing would be less than perfect. In particular, Hmax was significantly lower for comparisons between the two salty and the two sour tastants than for comparisons between a salty and sour tastant. In addition, within a given cell, tastants of similar qualities often evoked similar response magnitudes (at the concentrations used here) but different response magnitudes for tastants of dissimilar qualities (see Table 2). Thus it might be predicted that the spike count alone, Hcount, would be more informative for comparisons of tastants of dissimilar qualities. Present data also confirm this prediction. However, although the contribution of temporal coding was more frequent when spike counts were similar, the amount of information contributed by temporal coding could not be predicted by the average difference in response magnitude between any given pair of tastants. This result contradicts the strong form of our hypothesis that temporal coding is always used to discriminate among taste stimuli when differences in the number of spikes evoked by each of the tastants was insufficient to identify the tastant: there were many instances in which neither spike count nor temporal coding allowed discrimination.
The observation that temporal coding was evident for at least one pairwise comparison in nearly every cell was a surprise, given previous findings suggesting that only a subset of cells evidence temporal coding (Di Lorenzo and Victor 2003, 2007). Early studies of temporal coding in the CT nerve also found that only a subset of incoming fibers to the NTS convey information through the temporal characteristics of a response (Bradley et al. 1983; Mistretta 1972; Nagai and Ueda 1981). Other investigations in the CNS (Di Lorenzo and Schwartzbaum 1982; Katz et al. 2001; Nuding et al. 1991) have also implied that temporal coding is present in the responses of only a subset of cells. The use of taste stimuli that were of the same taste quality in the present study revealed that a larger proportion of NTS cells may convey information using the temporal characteristics of their responses than was previously thought (Di Lorenzo and Victor 2003, 2007). This suggests that most NTS cells may use temporal coding depending on the task required, e.g., distinguishing among very similar tasting stimuli.
The criteria that we used for determination of which responses displayed temporal coding were conservative, in the following sense. In our analyses, if spike count provided enough information to support perfect discrimination, then we ignored the possibility that temporal coding (by envelope or pattern) might also be used. Consequently, when both "temporal codes" and "count codes" led to perfect discrimination, the cell (or discrimination) was classified as using a count code. In these discriminations, performance based on spike count would be less than perfect had one made the task more challenging—such as by using a shorter presentation time or lower concentration—and this might have unmasked a contribution of temporal coding. Thus our analysis likely underestimates the number of cells that make use of temporal coding.
It is important to keep in mind that we investigated only pairwise discrimination of tastants; in nature, the gustatory system is faced with the problem of identifying a taste stimulus from a wider array of qualities. Taken together with previous observations (Di Lorenzo and Victor 2003), our results suggest that, for any given cell, the spike count can suffice for an "approximate" classification of a taste stimulus, but fine discriminations may rely on the temporal features of the response. For cells that respond equally well to tastants of different taste qualities, these results predict that temporal coding will take on a more prominent role. These results are in agreement with similar results of a study of contrast discrimination in the visual cortex (Reich et al. 2001). Specifically, Reich et al. (2001) found that spike count works well in the low and intermediate range of contrasts, but in the high-contrast range, where firing rates saturate, temporal coding plays an increasing role. Also in agreement with present results are data from the olfactory system of the honeybee, showing that disruption of synchronized activity in the antennal lobe of the brain adversely affects discrimination of similar, but not dissimilar, odorants (Stopfer 1997). Considered collectively, these data along with our own support the idea that temporal coding is most prominent when stimuli are difficult to discriminate from one another.
Implications and predictions
The present results are not inconsistent with either the labeled line or ANP theories of taste coding but rather add a dimension of complexity to both. For example, the reliance of the labeled line theory on the response characteristics of individual cells is fully consistent with the use of temporal coding to distinguish among tastants. However, the (now twice replicated; Di Lorenzo and Victor 2003, 2007) observation that response magnitudes are often highly variable across repeated trials suggests that discovering the "true" sensitivity profile of a cell may require multiple stimulus trials. It further suggests that, even if statistical differences in response magnitude can be identified, temporal patterns may be more reliable for discriminations based on single-trial responses. Moreover, there may be some cells that do not show straightforward preferences for tastants of a single quality. What role these cells play in taste coding is not explained by a labeled line conceptualization. Similarly, the ANP theory is also consistent with the use of temporal coding because it is possible to argue, as Katz has done (Katz et al. 2002), that temporal patterns of response are dynamic and evolve over the time course of the response.
Data from the present study also lead to some predictions about taste-related behavior. For example, results of the temporal coding analyses suggest that the behavioral discrimination of NaCl and LiCl (each at 0.1 M) would be possible, albeit difficult, using gustatory cues. Although it is possible to train rats to discriminate NaCl from LiCl at or near the concentrations used in the present study (Balgura and Smith 1970; Kieffer 1978; Nachman 1963; Ossenkopp et al. 1997; Strom 1970), many investigators believe this discrimination to be based on postingestional associations rather than taste per se. Contrary to this belief, Kiefer (1978) showed that rats can use taste cues alone to make behavioral distinctions between NaCl and LiCl at concentrations 
0.01 M. To our knowledge, similar data related to behavioral discrimination of HCl and citric acid are not available. However, present data suggest that these two tastants would be more easily discriminable than the two salts at the concentrations used here. This prediction is based on the observations that the average absolute difference in magnitudes of response to HCl and citric acid is larger than that between NaCl and LiCl, and that spike count accounts for all information available for the discrimination between HCl and citric acid in proportionately more cells than for the discrimination between NaCl and LiCl.
In conclusion, for any theory of neural coding in a perceptual system, the questions of what drives the respective neural elements to respond as they do and what targets of these elements read their signals are important ones to answer. In the case of the rat NTS, recent studies have shown that the incoming message to these cells originates from receptors that are specific for the various taste qualities (Chandreshakar et al. 2000; Huang et al. 2006; Zhang et al. 2003; Zhao et al. 2003). However, incoming fibers are known to diverge to synapse with many NTS cells (Whitehead and Frank 1983). Conversely, any given NTS cell receives input from fibers that may have a variety of taste sensitivities. As a result, NTS cells are more broadly tuned than CT fibers (Doetsch and Erickson 1970). Temporal characteristics of responses may represent a potential mechanism for producing more reliable outputs and expanding the communication capacity of individual cells. Regarding the targets of NTS cells, located mainly in the parabrachial nucleus of the pons and the reticular formation (Halsell et al. 1996), whether and how these cells can interpret temporal patterns of input are as yet unknown.
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FOOTNOTES |
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Address for reprint requests and other correspondence: P. M. Di Lorenzo, Dept. of Psychology, Binghamton University, Box 6000, Binghamton, NY 13902-6000 (E-mail: diloren{at}binghamton.edu)
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