TY - JOUR
AU1 - Yao, Justin D
AU2 - Sanes, Dan H
AB - Abstract Core auditory cortex (AC) neurons encode slow fluctuations of acoustic stimuli with temporally patterned activity. However, whether temporal encoding is necessary to explain auditory perceptual skills remains uncertain. Here, we recorded from gerbil AC neurons while they discriminated between a 4-Hz amplitude modulation (AM) broadband noise and AM rates >4 Hz. We found a proportion of neurons possessed neural thresholds based on spike pattern or spike count that were better than the recorded session’s behavioral threshold, suggesting that spike count could provide sufficient information for this perceptual task. A population decoder that relied on temporal information outperformed a decoder that relied on spike count alone, but the spike count decoder still remained sufficient to explain average behavioral performance. This leaves open the possibility that more demanding perceptual judgments require temporal information. Thus, we asked whether accurate classification of different AM rates between 4 and 12 Hz required the information contained in AC temporal discharge patterns. Indeed, accurate classification of these AM stimuli depended on the inclusion of temporal information rather than spike count alone. Overall, our results compare two different representations of time-varying acoustic features that can be accessed by downstream circuits required for perceptual judgments. amplitude modulation, auditory cortex, auditory discrimination, rate code, temporal code Introduction Perceptual judgments depend on neural responses that are unique to individual sensory stimuli. The neural representation can be as simple as the total spike count, or it can take on a more complicated form as the temporal distribution of spikes (temporal code). For example, visual and somatosensory cortex neurons encode behaviorally relevant stimulus parameters with a spike count code that provides sufficient information to guide perceptual acuity (Tolhurst et al. 1983; Parker and Hawken 1985; Bradley et al. 1987; Britten et al. 1992; Hernández et al. 2000; Salinas et al. 2000; Luna et al. 2005). However, most natural sounds are composed of time-varying intensity fluctuations, from slow (~1 Hz) to fast (>100 Hz), suggesting that a temporal pattern of activity may be required to perform fine perceptual judgments. For example, our ability to distinguish the pitch of a musical instrument must be encoded in the temporal domain, at least in the auditory brainstem (see Bidelman 2013 for review). Here, we ask whether temporal encoding by core auditory cortex (AC) neurons is necessary to explain behavioral acuity in animals that are discriminating sounds based on the modulation rate of time-varying intensity fluctuations. Modulation of signal amplitude is a fundamental acoustic cue that is present in speech, nonhuman vocalizations, and many other natural sounds (Shannon et al. 1995; Wang 2000; Singh and Theunissen 2003; Zeng et al. 2005; Elliott and Theunissen 2009). Although neural responses to amplitude modulated (AM) sounds are well characterized (Joris et al. 2004; Malone et al. 2010), their relationship to perceptual judgments is less certain. For very fast AM rates, core AC neurons are unable to synchronize to the stimulus, and must encode these stimuli with a spike count code (Yao and Sanes 2018). At intermediate AM rates, described perceptually as “flutter” (Miller and Taylor 1948), AC neurons can provide a sufficient representation through either spike count or temporal codes (Joris et al. 2004; Bendor and Wang 2007). In fact, the discrimination of large differences between temporal fluctuation rate in the flutter range may rely on an AC neuron spike count code (Lemus et al. 2009). In contrast, the peak of the AM spectrum of speech is quite slow at ~4 Hz (Ding et al. 2017). Thus, AC neuron temporal encoding could easily account for auditory discrimination of slow time-varying fluctuations. Here, we ask whether temporal encoding is necessary to explain behavioral acuity in animals performing an AM rate discrimination task. We recorded from gerbil AC neurons telemetrically while they discriminated between a 4-Hz AM broadband noise and AM rates >4 Hz. We found that a proportion of AC units displayed spike count AM discrimination thresholds that were superior to behavioral thresholds, suggesting that spike count is sufficiently informative to explain perceptual acuity. Similarly, a population-level activity decoder based on spike count was sufficient to explain average behavioral AM discrimination, whereas a decoder with access to temporal discharge information outperformed the best overall behavioral performance. Finally, we show that temporal coding is likely required to support the accurate classification of AM rates. Overall, our results suggest that discrimination of time-varying acoustic features can be accomplished with a spike count code, but categorization of these same stimuli requires temporal spike pattern information. Materials and Methods Experimental Subjects Three adult gerbils (Meriones unguiculatus, 2 males and 1 female) were weaned from commercial breeding pairs (Charles River), and housed on a 12 h light/dark cycle with free access to food and water unless otherwise noted. All procedures were approved by the Institutional Animal Care and Use Committee at New York University. Method Details Behavioral Apparatus Adult gerbils were placed in a plastic test cage (0.25 × 0.25 × 0.4 m) within a sound-attenuating booth (IAC; internal dimensions: 2.2 × 2 × 2 m) and observed via a closed-circuit monitor. Acoustic stimuli were delivered from a calibrated free-field tweeter (DX25TG0504; Vifa) positioned 1 m directly above the test cage. Sound calibration measurements were made with a 1/4-inch free-field condenser recording microphone (Bruël and Kjaer) placed in the center of the cage. A pellet dispenser (Med Associates Inc.) was connected to a customized 3D printed food tray placed within the test cage, and a nose port was placed on the opposite side. Stimulus, delivery of food pellet rewards (20 mg), and behavioral data acquisition were controlled by a personal computer through custom MATLAB scripts (written by Dr Daniel Stolzberg: https://github.com/dstolz/epsych) and an RZ6 multifunction processor (Tucker-Davis Technologies). Behavioral Training and Testing Amplitude modulation (AM) rate discrimination was assessed with a positive reinforcement Go-Nogo appetitive conditioning paradigm, similar to that as described previously (von Trapp et al. 2017). Briefly, gerbils were placed on controlled food access and trained to initiate a trial by placing their noses in a cylindrical port that interrupted an infrared beam. Animals were shaped to approach a food tray upon presentation of the “Go” signal (AM rate > 4 Hz), and received a reward (20-mg pellet) from a pellet dispenser (Med Associates Inc.). After learning to consistently initiate Go trials, animals were then trained to repoke upon presentation the “Nogo” signal (AM rate = 4 Hz). Nogo trials (30% probability) were randomly interleaved with Go trials. During the initial training stage, both the Go and Nogo stimuli consisted of AM frozen broadband noise (25-dB rolloff at 3.5 and 20 kHz) with a modulation depth of 100%, presented at a sound level of 50-dB SPL. In addition, AM stimuli were preceded by a 200-ms onset ramp, followed by an unmodulated period of 200 ms, which then transitioned to AM noise for at least 1000 ms. This resulted in a total stimulus duration of at least 1400 ms. Trials were scored as a Hit (correctly approaching the food tray during a Go trial), Miss (failing to approach the food tray and repoking during a Go trial), Correct Reject (CR; correctly repoking during a Nogo trial), or False Alarm (FA; incorrectly approaching the food tray on a Nogo trial). Psychometric thresholds were assessed by presenting Go trials across five different AM rates (4.5, 6, 8, 10, and 12 Hz), randomly interleaved with Nogo trials (4 Hz). The percentage of Hits were plotted as a function of AM rate and these psychometric functions were fit with a cumulative Gaussian using Bayesian inference from the open-source package psignifit 4 for MATLAB (Schütt et al. 2016). The fitted distribution of percent correct scores was then transformed to the signal detection metric, d′, by calculating the difference in z-scores of Hit rate versus FA rate (Green and Swets 1966). Hit and FA rates were constrained to floor (0.05) and ceiling (0.95) values to avoid d′ values that approach infinity. Psychometric threshold was defined as the AM rate at which d′ = 1. Only sessions during which the FA rate was ≤30% and the animal performed a minimum of 150 trials were used to track psychometric performance and auditory cortex physiology. Neurophysiology Electrophysiological procedures are identical to those of previous studies from our laboratory (Yao and Sanes 2018). Below, we provide a summary of the procedures. Electrode Implantation Animals underwent electrode implantation after they were fully trained and three psychometric functions had been obtained that met the criteria of FA rate ≤ 0.30 and maximum d′ ≥ 2. During implantation surgery, the animal was anesthetized with isoflurane/O2, secured on a stereotaxic device (Kopf), and a 16-channel silicone probe array (four shanks with recording sites arranged in a 600 × 600-μm grid; Neuronexus A4 × 4–4 mm-200-200-1250-H16_21 mm) was implanted in the left core auditory cortex. The array was fixed to a custom-made microdrive to allow for subsequent advancement across recording sessions, and angled at 25° in the mediolateral plane. Typically, we positioned the rostral-most shank of the array at 3.9 mm rostral and 4.6–4.8 mm lateral to lambda. A ground wire was inserted in the contralateral cortical hemisphere. Animals recovered for at least 1 week before being placed on controlled food access for psychometric testing. At the termination of each experiment, animals were deeply anesthetized with sodium pentobarbital (150 mg/kg) and electrolytic lesions were made through one contact site via passing current (7 mA, 5–10 s). Animals were then perfused with phosphate-buffered saline and 4% paraformaldehyde. Brains were extracted, postfixed, sectioned on a vibratome (Leica), and stained for Nissl for offline verification that electrode tracks spanned core auditory cortex (Radtke-Schuller et al. 2016). Data Acquisition Neural recordings were made from awake, behaving animals during psychometric testing. Extracellular neural activity was acquired via a 15-channel wireless headstage and receiver (model W16, Triangle Biosystems). The analog signals were preamplified and digitized at a 24.414 kHz sampling rate (TB32; Tucker-Davis Technologies). The converted digital signals were then fed via fiber optic link to the RZ5 base station (TDT, Tucker-Davis Technologies) for filtering and processing. For offline multiunit and single-unit analysis, signals were high-pass filtered (300 Hz), and common average referencing was applied to each individual channel (Ludwig et al. 2009). A spike extraction threshold was set to 4 SDs > noise floor, and an artifact rejection threshold was set to 20 SDs > noise floor. Candidate waveforms were then peak-aligned, hierarchically clustered, and sorted in principal component (PC) space using the MATLAB-based package UltraMegaSort 2000 (Fee et al. 1996; Hill et al. 2011). Well-isolated single units demonstrated a clear separation in PC space, and fewer than 10% of refractory period violations. The majority of recording sites contained spikes from several unresolved units and were considered multiunits. Separate analyses of single- versus multiunit populations revealed no systematic differences from one another, and were pooled for all reported analyses. Neurometric Classifiers We adopted spike count and pattern classifier analyses to further assess the cortical encoding of AM rates (Machens et al. 2003; Narayan et al. 2006; Wang et al. 2007; Billimoria et al. 2008; Schneider and Woolley 2010; von Trapp et al. 2016; Yao and Sanes 2018). The spike count metric used the overall spike count in response to each AM stimulus (1000 ms), whereas the spike pattern metric utilized Euclidean distance to quantify the dissimilarity between two spike trains in high-dimensional space (van Rossum 2001). Both spike count and spike pattern classifiers were decoded using a leave-one-out template-matching procedure. For each individual unit, test trials consisted of one randomly selected spike train from a Go trial at a particular AM rate (e.g., 4.5, 6, 8, 10, or 12 Hz), and one randomly selected spike train from a Nogo trial (4 Hz). Each Go and Nogo template was composed of all other trials other than the test trials. The test trial was assigned to the Go or Nogo template based on the smallest difference in spike count (spike count classifier) or Euclidean distance (spike pattern classifier) between the test and mean of the template trials. For classifying spike patterns, the average discriminability across all units was maximized when spike times were binned at 10 ms (Fig. 3C,D). Thus, all reported spike pattern data were from spike trains binned at 10 ms. Test and template trials were selected randomly, and spike train classification was repeated 1000 times to minimize selection biases. Classification of trial to template assignments was scored as follows: Go test trials were labeled as “Hits” or “Misses” if they were assigned to the Go or Nogo template, respectively. Likewise, Nogo test trials were labeled “False Alarms” or “Correct Rejections” if they were assigned to the Go or Nogo template, respectively (Fig. 3A). The percentage of Hit and FA scores were calculated across repetitions. The percentage of Hits was fit with a similar cumulative Gaussian as described in the psychometric analysis above. The fitted distribution of percent correct scores was then normalized (Z scored) and converted to a neural classifier-based d′. Neurometric thresholds for individual units were defined as the lowest AM rate that proved significantly different from the Nogo AM rate (4 Hz). Our procedural definition of significant neural AM rate discrimination was identical to that used for behavior, d′ ≥ 1. Thus, the neural threshold was defined as the AM rate at which the neurometric function crossed d′ = 1 (see Fig. 3B). Population Coding We used a previously employed linear classifier readout procedure (Yao and Sanes 2018) to assess AM rate discriminability across a population of AC single units. Specifically, a linear classifier was trained to decode responses from a proportion of trials to each stimulus set (e.g., “Go” and “Nogo”; Fig. 7A). Specifically, spike count responses from N neurons were counted across 1 ms bins to T trials of S stimuli and formed the population “response vector.” Since the number of trials were unequal across all units, we randomly subsampled a proportion of trials (i.e., 14 trials) from each unit. 13 of the 14 trials were then randomly sampled (without replacement) across N neurons and averaged to reduce the response vector to length Nbin. To decode overall spike count responses, spike counts were first summed across the bins, which further reduced the length of the response vector and eliminated the temporal dimension. A support vector machine (SVM) procedure was used to fit a linear hyperplane to the data set (“training set”). Cross-validated classification performance was assessed on the remaining single trial (1 of the 14) by computing the number of times this test set was correctly classified and misclassified based on the linear hyperplane across 500 iterations with a new randomly drawn sampled train and test sets for each iteration. Performance metrics included the proportion of correctly classified Go trials (“Hits”) and misclassified Nogo trials (“False Alarms”). Similar to the psychometric and individual unit neurometric analyses, we converted population decoder performance metrics into d′ values. Decoding readout performance was assessed as a function of the number of single units (Fig. 7B–F). The SVM procedure was implemented in MATLAB using the “fitcsvm” and “predict” functions with the “KernelFunction” set to “linear.” Experimental Design and Statistical Analysis Each experiment was performed once with technical replication occurring for behavioral data only (i.e., each animal was tested psychometrically multiple times), and all measures were subject to biological replication. Statistical analyses and procedures were implemented in JMP 13.2.0 (SAS) or custom-written MATLAB scripts (MathWorks) that incorporated the MATLAB Statistics Toolbox. For normally distributed data (as assessed by the Lilliefors test), data are reported as mean ± SEM unless otherwise stated. When data were not normally distributed, the nonparametric Wilcoxon signed-rank test was used when appropriate. Results Psychometric Sensitivity to AM Rate In order to simultaneously record from auditory cortex neurons during behavior, we first trained gerbils (n = 3) on a Go-Nogo AM rate discrimination task. Figure 1A displays an example psychometric function for one test session from one animal. AM discrimination thresholds were taken as the lowest AM rate corresponding to d′ = 1 from the fitted psychometric function. Across our three animals, AM discrimination thresholds were similar (two-way mixed model ANOVA; F(2,58) = 0.38, P = 0.69) (Fig. 1B), and the average AM rate discrimination threshold across all animals and sessions was 4.87 ± 0.02 Hz (relative to the 4 Hz Nogo stimulus). For each animal, thresholds were not statistically correlated with session number (Spearman’s correlation; Gerbil 1, r = 0.41, P = 0.05; Gerbil 2, r = 0.27, P = 0.09; Gerbil 3, r = 0.07, P = 0.60). We also measured lapse rate, or the probability of a Miss on the easiest Go signals (i.e., 12-Hz trials). Lapse rate has been used as a proxy for task engagement and motivation, as unmotivated animals tend to miss easy Go trials. No between-animal differences were observed for FA rate (two-way mixed model ANOVA; F(2,58) = 3.2, P = 0.50), lapse rate (two-way mixed model ANOVA; F(2,58) = 1.17, P = 0.32), maximum d′ (two-way mixed model ANOVA; F(2,58) = 2.72, P = 0.07), and reported “Yes” (two-way mixed model ANOVA; F(2,58) = 0.06, P = 0.94) (Fig. 1C). Figure 1 Open in new tabDownload slide Behavioral performance on the AM rate discrimination task. (A) Exemplar fit psychometric function obtained from one gerbil during one session. Horizontal black dashed line indicates discrimination threshold relative to the 4 Hz Nogo signal (d′ = 1). (B) Individual (symbols) psychometric thresholds across session number. Each symbol type corresponds to each individual gerbil. Average psychometric threshold from all animals (horizontal bar) is plotted. (C) Distribution of FA rate, lapse rate, maximum d′, and report “Yes” are plotted for each animal. Figure 1 Open in new tabDownload slide Behavioral performance on the AM rate discrimination task. (A) Exemplar fit psychometric function obtained from one gerbil during one session. Horizontal black dashed line indicates discrimination threshold relative to the 4 Hz Nogo signal (d′ = 1). (B) Individual (symbols) psychometric thresholds across session number. Each symbol type corresponds to each individual gerbil. Average psychometric threshold from all animals (horizontal bar) is plotted. (C) Distribution of FA rate, lapse rate, maximum d′, and report “Yes” are plotted for each animal. Behavioral Performance More Closely Matches Neural Sensitivity Based on Temporal Spike Patterns Recorded physiological data (Fig. 2A) were preprocessed to extract candidate waveforms for offline spike sorting procedures (see Materials and Methods). Principal component (PC) clustering (Fig. 2B) was used to further sort the extracted waveforms into clusters classified as single- or multiunits. Figure 2C displays example raster plots and corresponding poststimulus-time histograms (PSTHs) for one unit in response to each AM rate presented during task performance. We recorded from a total of 463 units (gerbil 1: 102, gerbil 2: 104, gerbil 3: 257) where 98 (21%) were classified as single units. Figure 2 Open in new tabDownload slide Candidate waveform selection for neurometric analyses. (A) Raw waveform trace of evoked neural response to AM stimulus. Red line represents selection criteria of >4 SDs above the noise floor. (B) Principal component analysis plot where two waveform clusters (blue and orange) are separated. Raw waveforms and averages from two waveform clusters are displayed above. (C) Example rasters and PSTHs from one unit in response to each AM stimulus. Bin width: 10 ms. Figure 2 Open in new tabDownload slide Candidate waveform selection for neurometric analyses. (A) Raw waveform trace of evoked neural response to AM stimulus. Red line represents selection criteria of >4 SDs above the noise floor. (B) Principal component analysis plot where two waveform clusters (blue and orange) are separated. Raw waveforms and averages from two waveform clusters are displayed above. (C) Example rasters and PSTHs from one unit in response to each AM stimulus. Bin width: 10 ms. To assess the neural sensitivity of each unit, we applied a template-matching classifier analysis that calculates performance for each unit based on the similarity of spike count and spike pattern to a template (see Materials and Methods; Fig. 3A). Neural sensitivity was quantified by a d′ metric that signifies the statistical difference between neural responses evoked by 4 Hz (Nogo signal) versus each Go signal (4.5–12 Hz). Figure 3B displays two neurometric functions, calculated by temporal spike pattern (green) and spike count (magenta) across 1000 ms stimulus duration, from one individual unit. For this example unit, d′ values were greater when calculated from the spike pattern metric compared to the spike count metric, suggesting spike pattern yields greater sensitivity. Figure 3 Open in new tabDownload slide Quantifying AM rate sensitivity with AC spike count and pattern. (A) Schematic of template-matching classification procedure. Spike count and spike pattern classifiers were decoded using a leave-one-out template-matching procedure. For each unit, test trials consisted of one randomly selected spike train from a Go trial at a particular AM rate (e.g., 4.5, 6, 8, 10, or 12 Hz), and one randomly selected spike train from a Nogo trial (4 Hz). Each template was composed of all other trials other than the test trial. The test trial was assigned to the Go or Nogo template based on the smallest difference in spike count (spike count classifier) or Euclidean distance (spike pattern classifier) between the test and mean of the template trials. This classification procedure was repeated 1000 times to minimize selection biases. See Methods for details. (B) Exemplar fit neurometric function from one unit based on spike pattern (green) and spike count (magenta) classification across 1000-ms stimulus duration. Horizontal black dashed line indicates discrimination threshold relative to the 4-Hz Nogo signal (d′ = 1). Corresponding thresholds for each classification metric are indicated by vertical dashed lines (spike pattern, green; spike count, magenta). (C) Scatter plot of best spike pattern d′ versus best spike count d′ across all individual units (circles). Histograms plot the distribution of best spike pattern and spike count d′. Inset: Average spike pattern best d′ (±SEM) as a function of bin width for all units. (D) Scatter plot of neural thresholds based on spike pattern and spike count metrics. Histograms plot the distribution of neural thresholds based on spike pattern and spike count. Inset: Average spike pattern neural threshold (±SEM) as a function of bin width for all units. (E) Cumulative distribution of thresholds for each classification metric. Vertical gray bar represents the average behavioral threshold. See text for statistical details. Figure 3 Open in new tabDownload slide Quantifying AM rate sensitivity with AC spike count and pattern. (A) Schematic of template-matching classification procedure. Spike count and spike pattern classifiers were decoded using a leave-one-out template-matching procedure. For each unit, test trials consisted of one randomly selected spike train from a Go trial at a particular AM rate (e.g., 4.5, 6, 8, 10, or 12 Hz), and one randomly selected spike train from a Nogo trial (4 Hz). Each template was composed of all other trials other than the test trial. The test trial was assigned to the Go or Nogo template based on the smallest difference in spike count (spike count classifier) or Euclidean distance (spike pattern classifier) between the test and mean of the template trials. This classification procedure was repeated 1000 times to minimize selection biases. See Methods for details. (B) Exemplar fit neurometric function from one unit based on spike pattern (green) and spike count (magenta) classification across 1000-ms stimulus duration. Horizontal black dashed line indicates discrimination threshold relative to the 4-Hz Nogo signal (d′ = 1). Corresponding thresholds for each classification metric are indicated by vertical dashed lines (spike pattern, green; spike count, magenta). (C) Scatter plot of best spike pattern d′ versus best spike count d′ across all individual units (circles). Histograms plot the distribution of best spike pattern and spike count d′. Inset: Average spike pattern best d′ (±SEM) as a function of bin width for all units. (D) Scatter plot of neural thresholds based on spike pattern and spike count metrics. Histograms plot the distribution of neural thresholds based on spike pattern and spike count. Inset: Average spike pattern neural threshold (±SEM) as a function of bin width for all units. (E) Cumulative distribution of thresholds for each classification metric. Vertical gray bar represents the average behavioral threshold. See text for statistical details. To assess which template-matching classifier metric yielded overall greater sensitivity, we compared each unit’s best (e.g., maximum) spike pattern d′ with its corresponding best spike count d′. These metrics were calculated across the entire 1000 ms stimulus duration. Across our population of recorded units, best spike pattern d′ (mean ± SE: 1.54 ± 0.04) was significantly higher than best spike count d′ (mean ± SE: 0.94 ± 0.02) (two-tailed t-test; P < 0.0001, t = 15.6) (Fig. 3C). To further examine neural sensitivity between spike pattern and spike count metrics, we compared each unit’s “neural threshold” extracted from spike pattern and spike count neurometric functions (Fig. 3D). We found that 16% of units produced neural thresholds based on spike count, whereas 58% of units produced neural thresholds based on spike pattern. Of the units with neural thresholds from either spike pattern or spike count, spike pattern neural thresholds were significantly lower than spike count neural thresholds (Wilcoxon signed-rank test; P < 0.0001; Spike pattern median threshold: 4.54 Hz; spike count median threshold: 10.2 Hz) (Fig. 3E). Together, these results indicate that the temporal spike pattern of cortical responses provides greater neural sensitivity than spike count, which may be utilized for stimulus-driven behavioral performance. To examine whether temporal spike patterns or overall spike count evoked by the AM rates are sufficient to explain behavioral performance, we quantified the relationship between neural and behavioral thresholds by calculating neural/behavioral threshold ratios for each unit. Specifically, each unit’s spike pattern and spike count neural threshold (Fig. 4A) is directly compared with its corresponding behavioral threshold from the same session. The distribution of spike pattern neural (neuralSP)/behavioral threshold and spike count (neuralSC)/behavioral threshold ratios for each animal are shown in Figure 4B. Behavioral thresholds more closely matched spike pattern neural thresholds (median neuralSP/behavioral threshold ratio: 0.97) than spike count neural thresholds (median neuralSC/behavioral threshold ratio: 1.3). Overall, neuralSP/behavioral threshold ratios were significantly lower than neuralSC/behavioral threshold ratios (Wilcoxon signed-rank test; P < 0.0001). Spike pattern neural thresholds could be better than behavioral thresholds. This is illustrated by the greater proportion of units with spike pattern neural thresholds ≤ behavioral thresholds (neuralSP/behavioral threshold ratio ≤ 1: 0.60, 162/272 units) relative to spike count neural thresholds ≤ behavioral thresholds (neuralSC/behavioral threshold ratio ≤ 1: 0.21, 17/80 units) (Fig. 4C). Figure 4 Open in new tabDownload slide Behavioral acuity is matched by AC sensitivity. (A) Histogram of neural thresholds from spike pattern (green) and spike count (magenta) metrics. Vertical gray bar represents average behavior threshold. (B) Relationship between AC activity and behavior quantified as the ratio between neural and behavior thresholds (NT/BT) from the same recorded sessions. Vertical lines represent median ratio values. (C) Proportion of individual units with ratio values ≤1. Neural classification metrics were calculated across 1000-ms stimulus duration. See text for statistical details. Figure 4 Open in new tabDownload slide Behavioral acuity is matched by AC sensitivity. (A) Histogram of neural thresholds from spike pattern (green) and spike count (magenta) metrics. Vertical gray bar represents average behavior threshold. (B) Relationship between AC activity and behavior quantified as the ratio between neural and behavior thresholds (NT/BT) from the same recorded sessions. Vertical lines represent median ratio values. (C) Proportion of individual units with ratio values ≤1. Neural classification metrics were calculated across 1000-ms stimulus duration. See text for statistical details. To examine whether greater neurometric sensitivity based on spike pattern relative to spike count could be explained by the degree of overall synchrony of each unit’s responses to AM rates, we compared each unit’s best vector strength against its best spike pattern d′ (Fig. 5A) and best spike count d′ (Fig. 5B). Vector strength represents the strength of stimulus synchrony and range from 0 (no synchrony) to 1 (all spikes are identical phase) (Goldberg and Brown 1969). We found that best spike pattern d′ possessed a significant positive correlation with corresponding best vector strength (linear regression; R2 = 0.30, P < 0.0001), whereas best spike count d′ had a near-zero correlation with best vector strength (linear regression; R2 = 0.01, P > 0.05). This demonstrates that the synchronous patterns of neural responses evoked by the presented AM rates are a strong factor driving the neurometric sensitivity. Figure 5 Open in new tabDownload slide Greater neurometric sensitivity based on spike pattern relative to spike count could be explained in part by the degree of overall synchrony of each unit’s responses to AM rates. (A) Scatter plot of each individual unit’s best spike pattern d′ versus its corresponding best vector strength value. (B) Scatter plot of each individual unit’s best spike count d′ versus its corresponding best vector strength value. Black lines represent linear fits of the data. See text for statistical details. Figure 5 Open in new tabDownload slide Greater neurometric sensitivity based on spike pattern relative to spike count could be explained in part by the degree of overall synchrony of each unit’s responses to AM rates. (A) Scatter plot of each individual unit’s best spike pattern d′ versus its corresponding best vector strength value. (B) Scatter plot of each individual unit’s best spike count d′ versus its corresponding best vector strength value. Black lines represent linear fits of the data. See text for statistical details. An Auditory Cortex Population Readout Reveals Complementary Codes for AM Rate Discrimination Our current findings suggest that neural sensitivity based on stimulus-driven temporal spike patterns for individual units correlates more closely to behavioral performance than neural sensitivity based on overall spike count. To assess whether population-level encoding follows a similar neural code that contributes to behavior, we constructed linear classifiers using support vector machines (SVM) (see Materials and Methods). Briefly, Go versus Nogo AM rate discriminability was calculated across our recorded single-unit population (n = 98) with a linear population readout scheme. Our population linear classifiers were trained to decode responses from a proportion of trials to each individual Go versus Nogo stimulus pair (Fig. 6A). Similar to the individual unit-by-unit template-matching classifier scheme, the parameters of our linear classifier (i.e., comparing populations of each individual Go signal vs. the Nogo signal) were chosen because the animal’s goal was to indicate and report whether the Go signal differentiated from the Nogo (4 Hz). To decode population responses, spike trains from all neurons were organized across 1 ms bins throughout the full stimulus duration (1000 ms) for all trials. Thus, the SVM was given access to spiking information across the entire temporal domain in order to fit a linear hyperplane that best segregated the training data set. Additionally, the SVM was given only overall spike count information (i.e., spike counts were summed across all bins throughout the entire stimulus duration) to fit its appropriate linear hyperplane. This reduced the length of the response vector and eliminated the temporal dimension. Cross-validated classification performance was assessed across 500 iterations and labeled as spike pattern and spike count readouts based on whether or not information within the temporal domain was present for the SVM, respectively. Overall, population decoding performance was assessed as a function of the number of units used in the linear population readout by applying a resampling procedure to randomly select a subpopulation of cells (5–98 at increasing increments of 5) across 250 iterations. During each iteration of the resampling procedure, a new subpopulation of cells was randomly selected (without replacement) prior to the decoding readout procedure. Thus, 250 groups of N cells from the entire population were randomly drawn and 500 sets of trials were randomly drawn. Figure 6 Open in new tabDownload slide AC population decoder analyses can explain behavioral performance. (A) Assessing population encoding by measuring discriminability with a linear population readout. See Methods for details. (B–F) Average (±SD) population decoder performance between AM rate Nogo (4 Hz) versus Go (4.5, 6, 8, 10, and 12 Hz) signals as a function of unit count. Green functions represent average readout performance from a population decoder with access to temporal discharge information. Magenta functions represent average readout performance from a population decoder based on overall spike count. Solid horizontal lines represent the best behavioral d′ from all animals and sessions. Dashed horizontal lines represent average behavioral d′ from all animals and sessions. Shaded region represents ±1 SD. Figure 6 Open in new tabDownload slide AC population decoder analyses can explain behavioral performance. (A) Assessing population encoding by measuring discriminability with a linear population readout. See Methods for details. (B–F) Average (±SD) population decoder performance between AM rate Nogo (4 Hz) versus Go (4.5, 6, 8, 10, and 12 Hz) signals as a function of unit count. Green functions represent average readout performance from a population decoder with access to temporal discharge information. Magenta functions represent average readout performance from a population decoder based on overall spike count. Solid horizontal lines represent the best behavioral d′ from all animals and sessions. Dashed horizontal lines represent average behavioral d′ from all animals and sessions. Shaded region represents ±1 SD. Spike pattern and spike count decoding performance for each Go versus Nogo condition is plotted as a function of the number of cells in Figure 6B–F. Across each stimulus condition, both spike pattern and spike count decoders displayed greater d′ with increasing cell counts. However, the spike pattern decoder outperforms the spike count decoder across all conditions (mean ± SEM d′ difference; 4 vs. 4.5 Hz: 1.89 ± 0.05; 4 vs. 6 Hz: 1.57 ± 0.05; 4 vs. 8 Hz: 1.45 ± 0.06; 4 vs. 10 Hz: 1.15 ± 0.07; 4 vs. 12 Hz: 1.10 ± 0.08). The spike pattern decoder reached maximum d′ for all stimulus conditions at ≥45 cells, whereas spike count decoder performance never reached an asymptote, suggesting that decoding performance could continue increasing with additional cells. To compare population coding with behavioral performance, we examined spike pattern and spike count decoder results relative to the overall best individual or average behavioral performance for each stimulus condition. Across all stimulus conditions (4 vs. 4.5, 6, 8, 10, and 12 Hz), the spike pattern decoder performed better than the average and best behavioral d′. The spike count decoder reached average behavioral d′ and only performed better than the best behavioral d′ for the near-threshold 4.5 Hz Go condition. AM Rate Classification Relies on the Temporal Patterns of Cortical Responses Currently, our results suggest that temporal and rate codes could serve as potential readouts for AM rate discrimination. This complementary neural code scheme could be most appropriate to our Go-Nogo AM rate discrimination task where a correct response could be determined based on the difference in evoked spike count or temporal pattern responses between a Go and Nogo signal. Thus, in order to distinguish the contribution of temporal versus spike count coding to AM rate processing, we asked: what sound-driven behavior would rely only on the temporal patterns of cortical responses? To address this, we predicted that an auditory classification task, where a subject is required to classify an AM rate stimulus across a number of various AM rates, would exclusively rely on the temporal patterns of cortical spikes for accurate behavioral performance. To test this prediction, we performed a template-matching classifier analysis on our current neural data set that calculates the classification accuracy for each unit based on the similarity of spike pattern and spike count to different AM rate templates. This is similar to our Go versus Nogo template-matching classifier analysis presented in the previous sections except test trials are compared with each of the 6 AM rate signals. Figure 7A displays dot rasters and corresponding PSTHs to each AM rate stimulus from one example unit. Classification performance based on temporal spike pattern and spike count from this example unit are displayed by confusion matrix plots in Figure 7B,C, respectively. For this example unit, AM rate classification based on temporal spike pattern is near perfect (Fig. 7B), whereas AM rate classification based on spike count is poor (Fig. 7C). This trend is evident across all units, with the grand mean confusion matrix based on spike pattern displaying near perfect AM rate classification (Fig. 7D). AM rate classification based on spike count remains poor (Fig. 7E). Figure 7 Open in new tabDownload slide Accurate classification of AM rates requires temporal coding. (A) Example rasters and PSTHs from one unit in response to each AM stimulus. (B) AM rate decoded with a temporal spike pattern classifier from the spiking responses from one example unit. (C) AM rate decoded with a spike count classifier from the spiking responses from one example unit. (D) Same as B except from the average of all units. (E) Same as C except from the average of all units. (F) Distribution of root-mean squared error (RMSE) of classification based on temporal spike pattern (green) and spike count (magenta) for each AM rate. Vertical bars represent population averages. (G) Average classification RMSE based on temporal spike pattern for each AM rate across bin widths. See text for statistical details. Figure 7 Open in new tabDownload slide Accurate classification of AM rates requires temporal coding. (A) Example rasters and PSTHs from one unit in response to each AM stimulus. (B) AM rate decoded with a temporal spike pattern classifier from the spiking responses from one example unit. (C) AM rate decoded with a spike count classifier from the spiking responses from one example unit. (D) Same as B except from the average of all units. (E) Same as C except from the average of all units. (F) Distribution of root-mean squared error (RMSE) of classification based on temporal spike pattern (green) and spike count (magenta) for each AM rate. Vertical bars represent population averages. (G) Average classification RMSE based on temporal spike pattern for each AM rate across bin widths. See text for statistical details. To quantify classification performance, we considered the unsigned error magnitude (mean observed RMS error, “RMSE”) for each tested AM rate. Larger RMSE values signify greater error magnitudes. Figure 7F plots the distribution of RMSE from all units for spike pattern and spike count classification across each tested AM rate. RMSE values displayed a significant interaction between neural classification metric (spike pattern and spike count) and AM rate (two-way mixed model ANOVA; F(5,4620) = 107, P < 0.0001). Post hoc two-tailed t-tests (Holm-Bonferroni-corrected) indicated RSME values from spike pattern were significantly lower than RSME values from spike count across all tested AM rates (mean ± SEM; 4 Hz: spike pattern = 0.17 ± 0.02, spike count = 2.60 ± 0.04, P < 0.0001, t = 62.1; 4.5 Hz: spike pattern = 0.45 ± 0.02, spike count = 2.42 ± 0.03, P < 0.0001, t = 51.9; 6 Hz: spike pattern = 0.57 ± 0.02, spike count = 2.22 ± 0.02, P < 0.0001, t = 51.3; 8 Hz: spike pattern = 0.66 ± 0.03, spike count = 2.30 ± 0.02, P < 0.0001, t = 50.5; 10 Hz: spike pattern = 0.95 ± 0.04, spike count = 2.85 ± 0.03, P < 0.0001, t = 30.9; 12 Hz: spike pattern = 1.17 ± 0.05, spike count = 3.77 ± 0.05, P < 0.0001, t = 36.1). These results demonstrate that temporal spike pattern is the dominant neural code for the classification of AM rates that range between 4 to 12 Hz. Furthermore, we found that RSME grew significantly larger with faster AM rates (two-way mixed model ANOVA; F(1,924) = 13 245, P < 0.0001). This suggests that the temporal spike pattern becomes a less reliable code at higher AM rates. To examine the degree to which classification accuracy improves across increasing dimensions of the data, we compared average classification RMSE for each AM rate based on temporal pattern as a function of bin width (Fig. 7G). We found that RMSE significantly increases with increasing bin width (two-way mixed model ANOVA; F(8,4158) = 2434, P < 0.0001). This suggests that spike pattern can be represented in a more complex space than a simple spike count measure. Overall, accurate neural classification of slow AM rates requires temporal spike pattern information. Discussion Understanding the relationship between perceptual judgments and the neural representation of sensory stimuli remains challenging due to the breadth of candidate codes (Perkel and Bullock 1968). To address this question, we simultaneously measured the perceptual ability of gerbils to discriminate between slow AM rates while recording stimulus-evoked responses from AC neurons. Our primary goal was to determine whether temporal coding was necessary to explain behavioral acuity. Here, we report that AC neuron spike count coding is sufficiently informative to explain the gerbils’ behavioral AM discrimination thresholds. Since temporal coding far outstripped behavior, we asked whether this information would be required to support a more demanding perceptual task. In fact, our results demonstrate temporal coding would be needed for accurate classification of slow AM rates. Below, we discuss these findings within the context that distinct perceptual capabilities driven by time-varying acoustic cues likely require separate cortical codes. A Spike Count Code is Sufficient to Support AM Discrimination The detection, discrimination, or categorization of envelope cues could be based on either of two cardinal strategies: a spike count code or some type of temporal code. For auditory cortex, a spike count code has been proposed to account for AM depth detection threshold, as well as improved sensitivity as the AM depth increases (Liang et al. 2002; Johnson et al. 2012; Niwa et al. 2012, 2013, 2015; Rosen et al. 2012; von Trapp et al. 2016; Yao and Sanes 2018). In fact, a cortical spike count code correlates closely with the perceptual acuity of detecting AM stimuli (Niwa et al. 2012, 2013, 2015; von Trapp et al. 2016; Caras and Sanes 2017; Yao and Sanes 2018), despite the availability of a synchronized discharge pattern that also scales with modulation depth (Eggermont 1994; Middlebrooks 2008a, 2008b; Malone et al. 2010). AC neuron discharge rate can also vary across a narrow range of modulation frequencies (Schreiner and Urbas 1986, 1988; Schulze and Langner 1997; Liang et al. 2002). Thus, spike count coding could also support AM discrimination. For example, the discrimination between temporal fluctuation rates within the flutter range (~10–50 Hz) is plausibly explained by an AC neuron spike count code (Lemus et al. 2009). Our results indicate that neural AM rate discrimination thresholds based on the overall spike count are sufficient to account for behavioral thresholds obtained simultaneously during a recording session (Fig. 4B). Furthermore, a population decoder based on spike count matched, but did not exceed, the average behavioral performance (Fig. 7). In contrast, when neural thresholds were based on spike pattern, a greater number of AC unit thresholds exceeded behavioral thresholds (Fig. 4C). One possible explanation for this disparity is that, with additional training, animals begin to use this temporal information and reach superior behavioral thresholds. In fact, the single best psychometric sensitivity displayed during a single session was 4.57 Hz, nearly identical to that predicted by a temporal coding strategy. Such a scenario could also help to explain why animals reach exceptional perceptual performance following focused practice on a narrow task (Recanzone et al. 1992, 1993; Crist et al. 2001; Schoups et al. 2001; Beitel et al. 2003; Bao et al. 2004; Polley et al. 2006; Yan et al. 2014; Caras and Sanes 2017). Another possible explanation for the disparity between neural thresholds and behavioral acuity is that the integration of sensory encoded information across areas downstream of sensory cortex could accurately predict how well an animal performs on a given trial (Yao et al. 2020). This is primarily the case as perceptual judgments emerge from the temporal integration of sensory inputs downstream of primary sensory cortices (Fassihi et al. 2017). As sensory input ascends the cortical pathway, the timescale over which neurons encode information increases. For example, neurons within secondary auditory cortex encode and integrate acoustic information over longer durations than neurons in primary auditory cortex (Boemio et al. 2005; Bendor and Wang 2007; Scott et al. 2011). These longer integration times are suggested to correlate with perceptual attributes (DeWitt and Rauschecker 2012; de Heer et al. 2017). Thus, even if a physical stimulus is encoded accurately within sensory cortex, the lack or inappropriate integration of such sensory information across downstream pathways could lead to poorer behavioral performance. Classification Judgments Must Rely on a Temporal Code Although a cortical spike count code is sufficient to explain the detection and discrimination of envelope cues, a temporal code could be required for more demanding perceptual judgments, such as a feature classification. Previous investigations on the neural encoding principles of communication sounds offer evidence that auditory cortex processing and temporal coding underlie perception for complex time-varying acoustic cues such as speech and animal vocalizations. First, AC lesions lead to severely impaired processing of communication sounds (Heffner and Heffner 1986; Porter et al. 2011). Second, neurophysiological studies across species demonstrate that natural vocalization sounds are highly represented by AC discharge patterns (e.g., Wang et al. 1995; Narayan et al. 2006; Schnupp et al. 2006; Billimoria et al. 2008; Engineer et al. 2008; Mesgarani et al. 2008; Russ et al. 2008; Recanzone 2008; Walker et al. 2008; Huetz et al. 2009; Schneider and Woolley 2013; see Gaucher et al. 2013 for review). Similarly, electrocorticography (ECoG) recordings from human auditory cortex utilize high-dimensional algorithms based on temporal signals to decode distinct features of speech (Mesgarani et al. 2014; Moses et al. 2019; Oganian and Chang 2019; Yi et al. 2019). Third, temporal coding of such complex time-varying fluctuations of acoustic cues is correlated with behavioral performance (Engineer et al. 2008; Schneider and Woolley 2013). Thus, the spike-timing-based coding strategies that sufficiently represent complex time-varying acoustic stimuli could drive perceptual judgments. Although our animals performed a discrimination task in the current study, we asked whether accurate AM rate classification might require the temporally patterned responses of AC neurons. We report that precise classification of AM rates in the 4–12 Hz range could not be accomplished with an AC code based on spike count alone. Rather, access to a temporal code is required (Fig. 7). Thus, an important future direction would be to simultaneously record neural and behavioral measures underlying the classification of AM stimuli. At the level of the auditory cortex, it might be the case that the candidate codes for accurate classification could change. We predict that if behavioral classification of slow AM rates requires a temporal code, then behavioral classification accuracy will be high with very few errors. Overall, our findings are consistent with previous sensory encoding studies that suggest neural information represented within primary auditory cortex carries complementary and multiplexed spike count and spike pattern codes that are sufficient for correct stimulus discrimination and classification (Malone et al. 2015). Such complementary cortical codes may further transform to an exclusive spike count code along the ascending pathway (Yin et al. 2011; Zuo et al. 2015). Furthermore, our current results build on previous findings that show spike count coding is sufficient to explain perceptual function and provide new evidence that the behavioral acuity of discriminating slow, time-varying fluctuations of acoustic cues could be explained by an AC spike count code. Notes We thank members of the Sanes laboratory for constructive comments. Conflict of interest: The authors whose names are listed immediately above certify that they have no affiliations with or involvement in any organization or entity with any financial, or nonfinancial interest in the subject matter or materials discussed in this manuscript. The authors declare no competing interests. Funding National Institute on Deafness and Other Communication Disorders at the National Institute of Health (grant numbers K99DC018600 to J.D.Y.; R01DC011284 to D.H.S.). References Bao S , Chang EF, Woods J, Merzenich MM. 2004 . Temporal plasticity in the primary auditory cortex induced by operant perceptual learning . Nat Neurosci . 7 ( 9 ): 974 – 981 . doi: 10.1038/nn1293 . Google Scholar Crossref Search ADS PubMed WorldCat Beitel RE , Schreiner CE, Cheung SW, Wang X, Merzenich MM. 2003 . Reward-dependent plasticity in the primary auditory cortex of adult monkeys trained to discriminate temporally modulated signals . Proc Natl Acad Sci USA . 100 ( 19 ): 11070 – 11075 . doi: 10.1073/pnas.1334187100 . Google Scholar Crossref Search ADS PubMed WorldCat Bendor D , Wang X. 2007 . Differential neural coding of acoustic flutter within primate auditory cortex . Nat Neurosci . 10 ( 6 ): 763 – 771 . doi: 10.1038/nn1888 . Google Scholar Crossref Search ADS PubMed WorldCat Bidelman GM . 2013 . The role of the auditory brainstem in processing musically relevant pitch . Front Psychol . 4 : 264 . doi: 10.3389/fpsyg.2013.00264 . Google Scholar Crossref Search ADS PubMed WorldCat Billimoria CP , Kraus BJ, Narayan R, Maddox RK, Sen K. 2008 . Invariance and sensitivity to intensity in neural discrimination of natural sounds . J Neurosci . 28 ( 25 ): 6304 – 6308 . doi: 10.1523/JNEUROSCI.0961-08.2008 . Google Scholar Crossref Search ADS PubMed WorldCat Boemio A , Fromm S, Braun A, Poeppel D. 2005 . Hierarchical and asymmetric temporal sensitivity in human auditory cortices . Nat Neurosci . 8 : 389 – 395 . Google Scholar Crossref Search ADS PubMed WorldCat Bradley A , Skottun BC, Ohzawa I, Sclar G, Freeman RD. 1987 . Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior . J Neurophysiol . 57 ( 3 ): 755 – 772 . doi: 10.1152/jn.1987.57.3.755 . Google Scholar Crossref Search ADS PubMed WorldCat Britten KH , Shadlen MN, Newsome WT, Movshon JA. 1992 . The analysis of visual motion: a comparison of neuronal and psychophysical performance . J Neurosci . 12 : 4745 – 4765 . https://www.ncbi.nlm.nih.gov/pubmed/1464765. Google Scholar Crossref Search ADS PubMed WorldCat Caras ML , Sanes DH. 2017 . Top-down modulation of sensory cortex gates perceptual learning . Proc Natl Acad Sci USA . 114 ( 37 ): 9972 – 9977 . doi: 10.1073/pnas.1712305114 . Google Scholar Crossref Search ADS PubMed WorldCat Crist RE , Li W, Gilbert CD. 2001 . Learning to see: experience and attention in primary visual cortex . Nat Neurosci . 4 ( 5 ): 519 – 525 . doi: 10.1038/87470 . Google Scholar Crossref Search ADS PubMed WorldCat de Heer WA , Huth AG, Griffiths TL, Gallant JL, Theunissen FE. 2017 . The hierarchical cortical organization of human speech processing . J Neurosci . 37 : 6539 – 6557 . Google Scholar Crossref Search ADS PubMed WorldCat DeWitt I , Rauschecker JP. 2012 . Phoneme and word recognition in the auditory ventral stream . Proc Natl Acad Sci USA . 109 : E505 – E514 . Google Scholar Crossref Search ADS PubMed WorldCat Ding N , Patel AD, Chen L, Butler H, Luo C, Poeppel D. 2017 . Temporal modulations in speech and music . Neurosci Biobehav Rev . 81 ( Pt B ): 181 – 187 . doi: 10.1016/j.neubiorev.2017.02.011 . Google Scholar Crossref Search ADS PubMed WorldCat Eggermont JJ . 1994 . Temporal modulation transfer functions for AM and FM stimuli in cat auditory cortex. Effects of carrier type, modulating waveform and intensity . Hear Res . 74 ( 1–2 ): 51 – 66 . doi: 10.1016/0378-5955(94)90175-9 . Google Scholar Crossref Search ADS PubMed WorldCat Elliott TM , Theunissen FE. 2009 . The modulation transfer function for speech intelligibility . PLoS Comput Biol . 5 ( 3 ): e1000302 . doi: 10.1371/journal.pcbi.1000302 . Google Scholar Crossref Search ADS PubMed WorldCat Engineer CT , Perez CA, Chen YH, Carraway RS, Reed AC, Shetake JA, Jakkamsetti V, Chang KQ, Kilgard MP. 2008 . Cortical activity patterns predict speech discrimination ability . Nat Neurosci . 11 ( 5 ): 603 – 608 . doi: 10.1038/nn.2109 . Google Scholar Crossref Search ADS PubMed WorldCat Fassihi A , Akrami A, Pulecchi F, Schonfelder V, Diamond ME. 2017 . Transformation of perception from sensory to motor cortex . Curr Biol . 27 : 1585 , e6– 1596 . Google Scholar Crossref Search ADS PubMed WorldCat Fee MS , Mitra PP, Kleinfeld D. 1996 . Automatic sorting of multiple unit neuronal signals in the presence of anisotropic and non-Gaussian variability . J Neurosci Methods . 69 : 175 – 188 . doi: 10.1016/S0165-0270(96)00050-7 . Google Scholar Crossref Search ADS PubMed WorldCat Gaucher Q , Huetz C, Gourévitch B, Laudanski J, Occelli F, Edeline JM. 2013 . How do auditory cortex neurons represent communication sounds . Hear Res . 305 : 102 – 112 . doi: 10.1016/j.heares.2013.03.011 . Google Scholar Crossref Search ADS PubMed WorldCat Goldberg JM , Brown PB. 1969 . Response of binaural neurons of dog superior olivary complex to dichotic tonal stimuli: some physiological mechanisms of sound localization . J Neurophysiol . 32 : 613 – 636 . doi: 10.1152/jn.1969.32.4.613 . Google Scholar Crossref Search ADS PubMed WorldCat Green DM , Swets JA. 1966 . Signal detection theory and psychophysics . New York : Wiley . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Heffner HE , Heffner RS. 1986 . Effect of unilateral and bilateral auditory cortex lesions on the discrimination of vocalizations by Japanese macaques . J Neurophysiol . 56 ( 3 ): 683 – 701 . doi: 10.1152/jn.1986.56.3.683 . Google Scholar Crossref Search ADS PubMed WorldCat Hernández A , Zainos A, Romo R. 2000 . Neuronal correlates of sensory discrimination in the somatosensory cortex . Proc Natl Acad Sci USA . 97 ( 11 ): 6191 – 6196 . doi: 10.1073/pnas.120018597 . Google Scholar Crossref Search ADS PubMed WorldCat Hill DN , Mehta SB, Kleinfeld D. 2011 . Quality metrics to accompany spike sorting of extracellular signals . J Neurosci . 31 ( 24 ): 8699 – 8705 . doi: 10.1523/JNEUROSCI.0971-11.2011 . Google Scholar Crossref Search ADS PubMed WorldCat Huetz C , Philibert B, Edeline JM. 2009 . A spike-timing code for discriminating conspecific vocalizations in the thalamocortical system of anesthetized and awake Guinea pigs . J Neurosci . 29 ( 2 ): 334 – 350 . doi: 10.1523/JNEUROSCI.3269-08.2009 . Google Scholar Crossref Search ADS PubMed WorldCat Johnson JS , Yin P, O’Connor KN, Sutter ML. 2012 . Ability of primary auditory cortical neurons to detect amplitude modulation with rate and temporal codes: neurometric analysis . J Neurophysiol . 107 ( 12 ): 3325 – 3341 . doi: 10.1152/jn.00812.2011 . Google Scholar Crossref Search ADS PubMed WorldCat Joris PX , Schreiner CE, Rees A. 2004 . Neural processing of amplitude-modulated sounds . Physiol Rev . 84 ( 2 ): 541 – 577 . doi: 10.1152/physrev.00029.2003 . Google Scholar Crossref Search ADS PubMed WorldCat Lemus L , Hernández A, Romo R. 2009 . Neural codes for perceptual discrimination of acoustic flutter in the primate auditory cortex . Proc Natl Acad Sci USA . 106 ( 23 ): 9471 – 9476 . doi: 10.1073/pnas.0904066106 . Google Scholar Crossref Search ADS PubMed WorldCat Liang L , Lu T, Wang X. 2002 . Neural representations of sinusoidal amplitude and frequency modulations in the primary auditory cortex of awake primates . J Neurophysiol . 87 ( 5 ): 2237 – 2261 . doi: 10.1152/jn.2002.87.5.2237 . Google Scholar Crossref Search ADS PubMed WorldCat Ludwig KA , Miriani RM, Langhals NB, Joseph MD, Anderson DJ, Kipke DR. 2009 . Using a common average reference to improve cortical neuron recordings from microelectrode arrays . J Neurophysiol . 101 ( 3 ): 1679 – 1689 . doi: 10.1152/jn.90989.2008 . Google Scholar Crossref Search ADS PubMed WorldCat Luna R , Hernández A, Brody CD, Romo R. 2005 . Neural codes for perceptual discrimination in primary somatosensory cortex . Nat Neurosci . 8 ( 9 ): 1210 – 1219 . doi: 10.1038/nn1513 . Google Scholar Crossref Search ADS PubMed WorldCat Machens CK , Schütze H, Franz A, Kolesnikova O, Stemmler MB, Ronacher B, Herz AV. 2003 . Single auditory neurons rapidly discriminate conspecific communication signals . Nat Neurosci . 6 ( 4 ): 341 – 342 . doi: 10.1038/nn1036 . Google Scholar Crossref Search ADS PubMed WorldCat Malone BJ , Scott BH, Semple MN. 2010 . Temporal codes for amplitude contrast in auditory cortex . J Neurosci . 30 ( 2 ): 767 – 784 . doi: 10.1523/JNEUROSCI.4170-09.2010 . Google Scholar Crossref Search ADS PubMed WorldCat Malone BJ , Scott BH, Semple MN. 2015 . Diverse cortical codes for scene segmentation in primate auditory cortex . J Neurophysiol . 113 ( 7 ): 2934 – 2952 . doi: 10.1152/jn.01054.2014 . Google Scholar Crossref Search ADS PubMed WorldCat Mesgarani N , Cheung C, Johnson K, Chang EF. 2014 . Phonetic feature encoding in human superior temporal gyrus . Science . 343 ( 6174 ): 1006 – 1010 . doi: 10.1126/science.1245994 . Google Scholar Crossref Search ADS PubMed WorldCat Mesgarani N , David SV, Fritz JB, Shamma SA. 2008 . Phoneme representation and classification in primary auditory cortex . J Acoust Soc Am . 123 ( 2 ): 899 – 909 . doi: 10.1121/1.2816572 . Google Scholar Crossref Search ADS PubMed WorldCat Middlebrooks JC . 2008a . Auditory cortex phase locking to amplitude-modulated cochlear implant pulse trains . J Neurophysiol . 100 ( 1 ): 76 – 91 . doi: 10.1152/jn.01109.2007 . Google Scholar Crossref Search ADS WorldCat Middlebrooks JC . 2008b . Cochlear-implant high pulse rate and narrow electrode configuration impair transmission of temporal information to the auditory cortex . J Neurophysiol . 100 ( 1 ): 92 – 107 . doi: 10.1152/jn.01114.2007 . Google Scholar Crossref Search ADS WorldCat Miller GA , Taylor WG. 1948 . The perception of repeated bursts of noise . J Acoust Soc Am . 20 : 171 . Google Scholar Crossref Search ADS WorldCat Moses DA , Leonard MK, Makin JG, Chang EF. 2019 . Real-time decoding of question-and-answer speech dialogue using human cortical activity . Nat Commun . 10 ( 1 ): 3096 . doi: 10.1038/s41467-019-10994-4 . Google Scholar Crossref Search ADS PubMed WorldCat Narayan R , Graña G, Sen K. 2006 . Distinct time scales in cortical discrimination of natural sounds in songbirds . J Neurophysiol . 96 ( 1 ): 252 – 258 . doi: 10.1152/jn.01257.2005 . Google Scholar Crossref Search ADS PubMed WorldCat Niwa M , Johnson JS, O’Connor KN, Sutter ML. 2012 . Active engagement improves primary auditory cortical neurons’ ability to discriminate temporal modulation . J Neurosci . 32 ( 27 ): 9323 – 9334 . doi: 10.1523/JNEUROSCI.5832-11.2012 . Google Scholar Crossref Search ADS PubMed WorldCat Niwa M , Johnson JS, O’Connor KN, Sutter ML. 2013 . Differences between primary auditory cortex and auditory belt related to encoding and choice for AM sounds . J Neurosci . 33 ( 19 ): 8378 – 8395 . doi: 10.1523/JNEUROSCI.2672-12.2013 . Google Scholar Crossref Search ADS PubMed WorldCat Niwa M , O’Connor KN, Engall E, Johnson JS, Sutter ML. 2015 . Hierarchical effects of task engagement on amplitude modulation encoding in auditory cortex . J Neurophysiol . 113 ( 1 ): 307 – 327 . doi: 10.1152/jn.00458.2013 . Google Scholar Crossref Search ADS PubMed WorldCat Oganian Y , Chang EF. 2019 . A speech envelope landmark for syllable encoding in human superior temporal gyrus . Sci Adv . 5 ( 11 ): eaay6279 . doi: 10.1126/sciadv.aay6279 . Google Scholar Crossref Search ADS PubMed WorldCat Parker A , Hawken M. 1985 . Capabilities of monkey cortical cells in spatial-resolution tasks . J Opt Soc Am A . 2 ( 7 ): 1101 – 1114 . doi: 10.1364/josaa.2.001101 . Google Scholar Crossref Search ADS PubMed WorldCat Perkel DH , Bullock TH. 1968 . Neural coding . Neurosci Res Program Bull . 6 ( 3 ): 221 – 348 . Google Scholar OpenURL Placeholder Text WorldCat Polley DB , Steinberg EE, Merzenich MM. 2006 . Perceptual learning directs auditory cortical map reorganization through top-down influences . J Neurosci . 26 ( 18 ): 4970 – 4982 . doi: 10.1523/JNEUROSCI.3771-05.2006 . Google Scholar Crossref Search ADS PubMed WorldCat Porter BA , Rosenthal TR, Ranasinghe KG, Kilgard MP. 2011 . Discrimination of brief speech sounds is impaired in rats with auditory cortex lesions . Behav Brain Res . 219 ( 1 ): 68 – 74 . doi: 10.1016/j.bbr.2010.12.015 . Google Scholar Crossref Search ADS PubMed WorldCat Radtke-Schuller S , Schuller G, Angenstein F, Grosser OS, Goldschmidt J, Budinger E. 2016 . Brain atlas of the Mongolian gerbil (Meriones unguiculatus) in CT/MRI-aided stereotaxic coordinates . Brain Struct Funct . 221 ( Suppl 1 ): 1 – 272 . doi: 10.1007/s00429-016-1259-0 . Google Scholar Crossref Search ADS PubMed WorldCat Recanzone GH . 2008 . Representation of con-specific vocalizations in the core and belt areas of the auditory cortex in the alert macaque monkey . J Neurosci . 28 ( 49 ): 13184 – 13193 . doi: 10.1523/JNEUROSCI.3619-08.2008 . Google Scholar Crossref Search ADS PubMed WorldCat Recanzone GH , Merzenich MM, Jenkins WM, Grajski KA, Dinse HR. 1992 . Topographic reorganization of the hand representation in cortical area 3b owl monkeys trained in a frequency-discrimination task . J Neurophysiol . 67 ( 5 ): 1031 – 1056 . doi: 10.1152/jn.1992.67.5.1031 . Google Scholar Crossref Search ADS PubMed WorldCat Recanzone GH , Schreiner CE, Merzenich MM. 1993 . Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys . J Neurosci . 13 ( 1 ): 87 – 103 . https://www.ncbi.nlm.nih.gov/pubmed/8423485. Google Scholar Crossref Search ADS PubMed WorldCat Rosen MJ , Sarro EC, Kelly JB, Sanes DH. 2012 . Diminished behavioral and neural sensitivity to sound modulation is associated with moderate developmental hearing loss . PLoS One . 7 : e41514 . doi: 10.1371/journal.pone.0041514 . Google Scholar Crossref Search ADS PubMed WorldCat Russ BE , Ackelson AL, Baker AE, Cohen YE. 2008 . Coding of auditory-stimulus identity in the auditory non-spatial processing stream . J Neurophysiol . 99 ( 1 ): 87 – 95 . doi: 10.1152/jn.01069.2007 . Google Scholar Crossref Search ADS PubMed WorldCat Salinas E , Hernandez A, Zainos A, Romo R. 2000 . Periodicity and firing rate as candidate neural codes for the frequency of vibrotactile stimuli . J Neurosci . 20 ( 14 ): 5503 – 5515 . https://www.ncbi.nlm.nih.gov/pubmed/10884334. Google Scholar Crossref Search ADS PubMed WorldCat Schneider DM , Woolley SM. 2010 . Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain . J Neurophysiol . 103 ( 6 ): 3248 – 3265 . doi: 10.1152/jn.01131.2009 . Google Scholar Crossref Search ADS PubMed WorldCat Schneider DM , Woolley SM. 2013 . Sparse and background-invariant coding of vocalizations in auditory scenes . Neuron . 79 ( 1 ): 141 – 152 . doi: 10.1016/j.neuron.2013.04.038 . Google Scholar Crossref Search ADS PubMed WorldCat Schnupp JW , Hall TM, Kokelaar RF, Ahmed B. 2006 . Plasticity of temporal pattern codes for vocalization stimuli in primary auditory cortex . J Neurosci . 26 ( 18 ): 4785 – 4795 . doi: 10.1523/JNEUROSCI.4330-05.2006 . Google Scholar Crossref Search ADS PubMed WorldCat Schoups A , Vogels R, Qian N, Orban G. 2001 . Practising orientation identification improves orientation coding in V1 neurons . Nature . 412 ( 6846 ): 549 – 553 . doi: 10.1038/35087601 . Google Scholar Crossref Search ADS PubMed WorldCat Schreiner CE , Urbas JV. 1986 . Representation of amplitude modulation in the auditory cortex of the cat. I. The anterior auditory field (AAF) . Hear Res . 21 ( 3 ): 227 – 241 . doi: 10.1016/0378-5955(86)90221-2 . Google Scholar Crossref Search ADS PubMed WorldCat Schreiner CE , Urbas JV. 1988 . Representation of amplitude modulation in the auditory cortex of the cat. II. Comparison between cortical fields . Hear Res . 32 ( 1 ): 49 – 63 . doi: 10.1016/0378-5955(88)90146-3 . Google Scholar Crossref Search ADS PubMed WorldCat Schulze H , Langner G. 1997 . Periodicity coding in the primary auditory cortex of the Mongolian gerbil (Meriones unguiculatus): two different coding strategies for pitch and rhythm . J Comp Physiol A . 181 ( 6 ): 651 – 663 . doi: 10.1007/s003590050147 . Google Scholar Crossref Search ADS PubMed WorldCat Schütt HH , Harmeling S, Macke JH, Wichmann FA. 2016 . Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data . Vision Res . 122 : 105 – 123 . doi: 10.1016/j.visres.2016.02.002 . Google Scholar Crossref Search ADS PubMed WorldCat Scott BH , Malone BJ, Semple MN. 2011 . Transformation of temporal processing across auditory cortex of awake macaques . J Neurophysiol . 105 : 712 – 730 . Google Scholar Crossref Search ADS PubMed WorldCat Shannon RV , Zeng FG, Kamath V, Wygonski J, Ekelid M. 1995 . Speech recognition with primarily temporal cues . Science . 270 ( 5234 ): 303 – 304 . https://www.ncbi.nlm.nih.gov/pubmed/7569981. Google Scholar Crossref Search ADS PubMed WorldCat Singh NC , Theunissen FE. 2003 . Modulation spectra of natural sounds and ethological theories of auditory processing . J Acoust Soc Am . 114 ( 6 Pt 1 ): 3394 – 3411 . https://www.ncbi.nlm.nih.gov/pubmed/14714819. Google Scholar PubMed OpenURL Placeholder Text WorldCat Tolhurst DJ , Movshon JA, Dean AF. 1983 . The statistical reliability of signals in single neurons in cat and monkey visual cortex . Vision Res . 23 ( 8 ): 775 – 785 . https://www.cns.nyu.edu/~tony/Publications/tolhurst-movshon-dean-1983.pdf. Google Scholar Crossref Search ADS PubMed WorldCat van Rossum MC . 2001 . A novel spike distance . Neural Comput . 13 ( 4 ): 751 – 763 . https://www.ncbi.nlm.nih.gov/pubmed/11255567. Google Scholar Crossref Search ADS PubMed WorldCat von Trapp G , Aloni I, Young S, Semple MN, Sanes DH. 2017 . Developmental hearing loss impedes auditory task learning and performance in gerbils . Hear Res . 347 : 3 – 10 . doi: 10.1016/j.heares.2016.07.020 . Google Scholar Crossref Search ADS PubMed WorldCat von Trapp G , Buran BN, Sen K, Semple MN, Sanes DH. 2016 . A decline in response variability improves neural signal detection during auditory task performance . J Neurosci . 36 ( 43 ): 11097 – 11106 . doi: 10.1523/JNEUROSCI.1302-16.2016 . Google Scholar Crossref Search ADS PubMed WorldCat Walker KM , Ahmed B, Schnupp JW. 2008 . Linking cortical spike pattern codes to auditory perception . J Cogn Neurosci . 20 ( 1 ): 135 – 152 . doi: 10.1162/jocn.2008.20012 . Google Scholar Crossref Search ADS PubMed WorldCat Wang X . 2000 . On cortical coding of vocal communication sounds in primates . Proc Natl Acad Sci USA . 97 ( 22 ): 11843 – 11849 . doi: 10.1073/pnas.97.22.11843 . Google Scholar Crossref Search ADS PubMed WorldCat Wang X , Merzenich MM, Beitel R, Schreiner CE. 1995 . Representation of a species-specific vocalization in the primary auditory cortex of the common marmoset: temporal and spectral characteristics . J Neurophysiol . 74 ( 6 ): 2685 – 2706 . doi: 10.1152/jn.1995.74.6.2685 . Google Scholar Crossref Search ADS PubMed WorldCat Wang L , Narayan R, Graña G, Shamir M, Sen K. 2007 . Cortical discrimination of complex natural stimuli: can single neurons match behavior . J Neurosci . 27 (3): 582 – 589 . doi: 10.1523/JNEUROSCI.3699-06.2007 . Google Scholar Crossref Search ADS PubMed WorldCat Yan Y , Rasch MJ, Chen M, Xiang X, Huang M, Wu S, Li W. 2014 . Perceptual training continuously refines neuronal population codes in primary visual cortex . Nat Neurosci . 17 ( 10 ): 1380 – 1387 . doi: 10.1038/nn.3805 . Google Scholar Crossref Search ADS PubMed WorldCat Yao JD , Sanes DH. 2018 . Developmental deprivation-induced perceptual and cortical processing deficits in awake-behaving animals . Elife . 7 . doi: 10.7554/eLife.33891 . Google Scholar OpenURL Placeholder Text WorldCat Crossref Yao JD , Gimoto J, Constantinople CM, Sanes DH. 2020 . Parietal cortex is required for the integration of acoustic evidence . Curr Biol . 30(17): 3293–3303. doi: 10.1016/j.cub.2020.06.017 . Google Scholar OpenURL Placeholder Text WorldCat Crossref Yi HG , Leonard MK, Chang EF. 2019 . The encoding of speech sounds in the superior temporal gyrus . Neuron . 102 ( 6 ): 1096 – 1110 . doi: 10.1016/j.neuron.2019.04.023 . Google Scholar Crossref Search ADS PubMed WorldCat Yin P , Johnson JS, O’Connor KN, Sutter ML. 2011 . Coding of amplitude modulation in primary auditory cortex . J Neurophysiol . 105 ( 2 ): 582 – 600 . doi: 10.1152/jn.00621.2010 . Google Scholar Crossref Search ADS PubMed WorldCat Zeng FG , Nie K, Stickney GS, Kong YY, Vongphoe M, Bhargave A, Cao K. 2005 . Speech recognition with amplitude and frequency modulations . Proc Natl Acad Sci USA . 102 ( 7 ): 2293 – 2298 . doi: 10.1073/pnas.0406460102 . Google Scholar Crossref Search ADS PubMed WorldCat Zuo Y , Safaai H, Notaro G, Mazzoni A, Panzeri S, Diamond ME. 2015 . Complementary contributions of spike timing and spike rate to perceptual decisions in rat S1 and S2 cortex . Curr Biol . 25 ( 3 ): 357 – 363 . doi: 10.1016/j.cub.2014.11.065 . Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press 2021. This work is written by US Government employees and is in the public domain in the US. Published by Oxford University Press 2021.
TI - Temporal Encoding is Required for Categorization, But Not Discrimination
JF - Cerebral Cortex
DO - 10.1093/cercor/bhaa396
DA - 2021-05-10
UR - https://www.deepdyve.com/lp/oxford-university-press/temporal-encoding-is-required-for-categorization-but-not-E8BFCp4LMk
SP - 2886
EP - 2897
VL - 31
IS - 6
DP - DeepDyve
ER -