Neural Population Coding of Natural Sounds in Non-flying Mammals

2017 ◽  
Author(s):  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Cortical Neurons ◽  
Dominant Role ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Neural Population ◽  
Good Effect ◽  
Natural Sounds ◽  
Complex Sounds

Understanding the principles by which sensory systems represent natural stimuli is one of the holy grails of neuroscience. In the auditory system, the study of the coding of natural sounds has a particular prominence. Indeed, the relationships between neural responses to simple stimuli (usually pure tone bursts)—often used to characterize auditory neurons—and complex sounds (in particular natural sounds) may be complex. Many different classes of natural sounds have been used to study the auditory system. Sound families that researchers have used to good effect in this endeavor include human speech, species-specific vocalizations, an “acoustic biotope” selected in one way or another, and sets of artificial sounds that mimic important features of natural sounds. Peripheral and brainstem representations of natural sounds are relatively well understood. The properties of the peripheral auditory system play a dominant role, and further processing occurs mostly within the frequency channels determined by these properties. At the level of the inferior colliculus, the highest brainstem station, representational complexity increases substantially due to the convergence of multiple processing streams. Undoubtedly, the most explored part of the auditory system, in term of responses to natural sounds, is the primary auditory cortex. In spite of over 50 years of research, there is still no commonly accepted view of the nature of the population code for natural sounds in the auditory cortex. Neurons in the auditory cortex are believed by some to be primarily linear spectro-temporal filters, by others to respond to conjunctions of important sound features, or even to encode perceptual concepts such as “auditory objects.” Whatever the exact mechanism is, many studies consistently report a substantial increase in the variability of the response patterns of cortical neurons to natural sounds. The generation of such variation may be the main contribution of auditory cortex to the coding of natural sounds.

scholarly journals Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex

2009 ◽  
Vol 102 (6) ◽  
pp. 3329-3339 ◽  
Author(s):  
Nima Mesgarani ◽  
Stephen V. David ◽  
Jonathan B. Fritz ◽  
Shihab A. Shamma
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Receptive Fields ◽  
Primary Auditory Cortex ◽  
Neural Population ◽  
Stimulus Context ◽  
Sensory Stimuli ◽  
Complex Sounds ◽  
Population Responses ◽  
Stimulus Reconstruction

Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.

Temporal Coherence Sensitivity in Auditory Cortex

2002 ◽  
Vol 88 (5) ◽  
pp. 2684-2699 ◽  
Author(s):  
Dennis L. Barbour ◽  
Xiaoqin Wang
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Carrier Frequency ◽  
Cortical Neurons ◽  
Neuronal Response ◽  
Primary Auditory Cortex ◽  
Temporal Coherence ◽  
Natural Sounds ◽  
Frequency Changes ◽  
Modulation Phase

Natural sounds often contain energy over a broad spectral range and consequently overlap in frequency when they occur simultaneously; however, such sounds under normal circumstances can be distinguished perceptually (e.g., the cocktail party effect). Sound components arising from different sources have distinct (i.e., incoherent) modulations, and incoherence appears to be one important cue used by the auditory system to segregate sounds into separately perceived acoustic objects. Here we show that, in the primary auditory cortex of awake marmoset monkeys, many neurons responsive to amplitude- or frequency-modulated tones at a particular carrier frequency [the characteristic frequency (CF)] also demonstrate sensitivity to the relative modulation phase between two otherwise identically modulated tones: one at CF and one at a different carrier frequency. Changes in relative modulation phase reflect alterations in temporal coherence between the two tones, and the most common neuronal response was found to be a maximum of suppression for the coherent condition. Coherence sensitivity was generally found in a narrow frequency range in the inhibitory portions of the frequency response areas (FRA), indicating that only some off-CF neuronal inputs into these cortical neurons interact with on-CF inputs on the same time scales. Over the population of neurons studied, carrier frequencies showing coherence sensitivity were found to coincide with the carrier frequencies of inhibition, implying that inhibitory inputs create the effect. The lack of strong coherence-induced facilitation also supports this interpretation. Coherence sensitivity was found to be greatest for modulation frequencies of 16–128 Hz, which is higher than the phase-locking capability of most cortical neurons, implying that subcortical neurons could play a role in the phenomenon. Collectively, these results reveal that auditory cortical neurons receive some off-CF inputs temporally matched and some temporally unmatched to the on-CF input(s) and respond in a fashion that could be utilized by the auditory system to segregate natural sounds containing similar spectral components (such as vocalizations from multiple conspecifics) based on stimulus coherence.

Temporally precise population coding of dynamic sounds by auditory cortex

2021 ◽  
Author(s):  
Joshua D Downer ◽  
James Bigelow ◽  
Melissa Runfeldt ◽  
Brian James Malone
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Frequency Tuning ◽  
Average Rate ◽  
Relative Effectiveness ◽  
Primary Auditory Cortex ◽  
Population Based ◽  
Population Coding ◽  
Complex Sounds ◽  
Animal Vocalizations

Fluctuations in the amplitude envelope of complex sounds provide critical cues for hearing, particularly for speech and animal vocalizations. Responses to amplitude modulation (AM) in the ascending auditory pathway have chiefly been described for single neurons. How neural populations might collectively encode and represent information about AM remains poorly characterized, even in primary auditory cortex (A1). We modeled population responses to AM based on data recorded from A1 neurons in awake squirrel monkeys and evaluated how accurately single trial responses to modulation frequencies from 4 to 512 Hz could be decoded as functions of population size, composition, and correlation structure. We found that a population-based decoding model that simulated convergent, equally weighted inputs was highly accurate and remarkably robust to the inclusion of neurons that were individually poor decoders. By contrast, average rate codes based on convergence performed poorly; effective decoding using average rates was only possible when the responses of individual neurons were segregated, as in classical population decoding models using labeled lines. The relative effectiveness of dynamic rate coding in auditory cortex was explained by shared modulation phase preferences among cortical neurons, despite heterogeneity in rate-based modulation frequency tuning. Our results indicate significant population-based synchrony in primary auditory cortex and suggest that robust population coding of the sound envelope information present in animal vocalizations and speech can be reliably achieved even with indiscriminate pooling of cortical responses. These findings highlight the importance of firing rate dynamics in population-based sensory coding.

Sound frequency representation in primary auditory cortex is level tolerant for moderately loud, complex sounds

2011 ◽  
Vol 106 (2) ◽  
pp. 1016-1027 ◽  
Author(s):  
Martin Pienkowski ◽  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Field Potential ◽  
Primary Auditory Cortex ◽  
Sound Level ◽  
Sound Frequency ◽  
Frequency Resolution ◽  
Neural Population ◽  
Complex Sounds ◽  
Individual Unit ◽  
Nonlinear Processing

The distribution of neuronal characteristic frequencies over the area of primary auditory cortex (AI) roughly reflects the tonotopic organization of the cochlea. However, because the area of AI activated by any given sound frequency increases erratically with sound level, it has generally been proposed that frequency is represented in AI not with a rate-place code but with some more complex, distributed code. Here, on the basis of both spike and local field potential (LFP) recordings in the anesthetized cat, we show that the tonotopic representation in AI is much more level tolerant when mapped with spectrotemporally dense tone pip ensembles rather than with individually presented tone pips. That is, we show that the tuning properties of individual unit and LFP responses are less variable with sound level under dense compared with sparse stimulation, and that the spatial frequency resolution achieved by the AI neural population at moderate stimulus levels (65 dB SPL) is better with densely than with sparsely presented sounds. This implies that nonlinear processing in the central auditory system can compensate (in part) for the level-dependent coding of sound frequency in the cochlea, and suggests that there may be a functional role for the cortical tonotopic map in the representation of complex sounds.

scholarly journals Synaptic Mechanisms for Bandwidth Tuning in Awake Mouse Primary Auditory Cortex

2018 ◽  
Vol 29 (7) ◽  
pp. 2998-3009 ◽  
Author(s):  
Haifu Li ◽  
Feixue Liang ◽  
Wen Zhong ◽  
Linqing Yan ◽  
Lucas Mesik ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Primary Auditory Cortex ◽  
Cell Voltage ◽  
Inhibitory Neurons ◽  
Complex Sounds ◽  
Excitatory Neurons ◽  
Layer 4 ◽  
Bandwidth Tuning ◽  
Spatial Size

Abstract Spatial size tuning in the visual cortex has been considered as an important neuronal functional property for sensory perception. However, an analogous mechanism in the auditory system has remained controversial. In the present study, cell-attached recordings in the primary auditory cortex (A1) of awake mice revealed that excitatory neurons can be categorized into three types according to their bandwidth tuning profiles in response to band-passed noise (BPN) stimuli: nonmonotonic (NM), flat, and monotonic, with the latter two considered as non-tuned for bandwidth. The prevalence of bandwidth-tuned (i.e., NM) neurons increases significantly from layer 4 to layer 2/3. With sequential cell-attached and whole-cell voltage-clamp recordings from the same neurons, we found that the bandwidth preference of excitatory neurons is largely determined by the excitatory synaptic input they receive, and that the bandwidth selectivity is further enhanced by flatly tuned inhibition observed in all cells. The latter can be attributed at least partially to the flat tuning of parvalbumin inhibitory neurons. The tuning of auditory cortical neurons for bandwidth of BPN may contribute to the processing of complex sounds.

scholarly journals Selective effects of arousal on population coding of natural sounds in auditory cortex

2020 ◽  
Author(s):  
Charles R. Heller ◽  
Zachary P. Schwartz ◽  
Daniela Saderi ◽  
Stephen V. David
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Response Magnitude ◽  
Correlated Noise ◽  
Natural Sounds ◽  
Dimensional Changes ◽  
Neural Populations ◽  
Evoked Activity ◽  
Low Dimensional

AbstractThe ability to discriminate between complex natural sounds is critical for survival. Changes in arousal and other aspects of behavioral state can impact the accuracy of sensory coding, affecting both the reliability of single neuron responses and the degree of correlated noise between neurons. However, it is unclear how these effects interact to influence coding of diverse natural stimuli. We recorded the spiking activity of neural populations in primary auditory cortex (A1) evoked by a large library of natural sounds while monitoring changes in pupil size as an index of arousal. Heightened arousal increased response magnitude and reduced noise correlations between neurons, improving coding accuracy on average. Rather than suppressing shared noise along all dimensions of neural activity, the change in noise correlations occurred via coherent, low-dimensional modulation of response variability in A1. The modulation targeted a different group of neurons from those undergoing changes in response magnitude. Thus, changes in response magnitude and correlation are mediated by distinct mechanisms. The degree to which these low-dimensional changes were aligned with the high-dimensional natural sound-evoked activity was variable, resulting in stimulus-dependent improvements in coding accuracy.

scholarly journals Population Responses Represent Vocalization Identity, Intensity, and Signal-to-Noise Ratio in Primary Auditory Cortex

2019 ◽  
Author(s):  
Ruiye Ni ◽  
David A. Bender ◽  
Dennis L. Barbour
Keyword(s):  
Auditory Cortex ◽  
Single Unit ◽  
Temporal Dynamics ◽  
Population Analysis ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Single Units ◽  
Neural Population ◽  
Population Responses ◽  
Noise Ratio

AbstractThe ability to process speech signals under challenging listening environments is critical for speech perception. Great efforts have been made to reveal the underlying single unit encoding mechanism. However, big variability is usually discovered in single-unit responses, and the population coding mechanism is yet to be revealed. In this study, we are aimed to study how a population of neurons encodes behaviorally relevant signals subjective to change in intensity and signal-noise-ratio (SNR). We recorded single-unit activity from the primary auditory cortex of awake common marmoset monkeys (Callithrix jacchus) while delivering conspecific vocalizations degraded by two different background noises: broadband white noise (WGN) and vocalization babble (Babble). By pooling all single units together, the pseudo-population analysis showed the population neural responses track intra- and inter-trajectory angle evolutions track vocalization identity and intensity/SNR, respectively. The ability of the trajectory to track the vocalizations attribute was degraded to a different degree by different noises. Discrimination of neural populations evaluated by neural response classifiers revealed that a finer optimal temporal resolution and longer time scale of temporal dynamics were needed for vocalizations in noise than vocalizations at multiple different intensities. The ability of population responses to discriminate between different vocalizations were mostly retained above the detection threshold.Significance StatementHow our brain excels in the challenge of precise acoustic signal encoding against noisy environment is of great interest for scientists. Relatively few studies have strived to tackle this mystery from the perspective of neural population responses. Population analysis reveals the underlying neural encoding mechanism of complex acoustic stimuli based upon a pool of single units via vector coding. We suggest the spatial population response vectors as one important way for neurons to integrate multiple attributes of natural acoustic signals, specifically, marmots’ vocalizations.

scholarly journals Neural correlates of learning pure tones versus natural sounds in the auditory cortex

2018 ◽  
Author(s):  
Ido Maor ◽  
Ravid Shwartz-Ziv ◽  
Libi Feigin ◽  
Yishai Elyada ◽  
Haim Sompolinsky ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Perceptual Learning ◽  
Discrimination Performance ◽  
Primary Auditory Cortex ◽  
Neural Correlates ◽  
Natural Sounds ◽  
Complex Sounds ◽  
Tuning Curves ◽  
Pure Tones ◽  
Perceptual Limits

ABSTRACTAuditory perceptual learning of pure tones causes tonotopic map expansion in the primary auditory cortex (A1), but the function this plasticity sub-serves is unclear. We developed an automated training platform called the ‘Educage’, which was used to train mice on a go/no-go auditory discrimination task to their perceptual limits, for difficult discriminations among pure tones or natural sounds. Spiking responses of excitatory and inhibitory L2/3 neurons in mouse A1 revealed learning-induced overrepresentation of the learned frequencies, in accordance with previous literature. Using a novel computational model to study auditory tuning curves we show that overrepresentation does not necessarily improve discrimination performance of the network to the learned tones. In contrast, perceptual learning of natural sounds induced ‘sparsening’ and decorrelation of the neural response, and consequently improving discrimination of these complex sounds. The signature of plasticity in A1 highlights its central role in coding natural sounds as compared to pure tones.

Neural Responses in Primary Auditory Cortex Mimic Psychophysical, Across-Frequency-Channel, Gap-Detection Thresholds

2000 ◽  
Vol 84 (3) ◽  
pp. 1453-1463 ◽  
Author(s):  
Jos J. Eggermont
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Primary Auditory Cortex ◽  
Noise Burst ◽  
Gap Detection ◽  
Frequency Content ◽  
Neural Responses ◽  
Frequency Channel ◽  
Interval Representations ◽  
Onset Response

Responses of single- and multi-units in primary auditory cortex were recorded for gap-in-noise stimuli for different durations of the leading noise burst. Both firing rate and inter-spike interval representations were evaluated. The minimum detectable gap decreased in exponential fashion with the duration of the leading burst to reach an asymptote for durations of 100 ms. Despite the fact that leading and trailing noise bursts had the same frequency content, the dependence on leading burst duration was correlated with psychophysical estimates of across frequency channel (different frequency content of leading and trailing burst) gap thresholds in humans. The duration of the leading burst plus that of the gap was represented in the all-order inter-spike interval histograms for cortical neurons. The recovery functions for cortical neurons could be modeled on basis of fast synaptic depression and after-hyperpolarization produced by the onset response to the leading noise burst. This suggests that the minimum gap representation in the firing pattern of neurons in primary auditory cortex, and minimum gap detection in behavioral tasks is largely determined by properties intrinsic to those, or potentially subcortical, cells.

scholarly journals Spatial Sensitivity in Field PAF of Cat Auditory Cortex

2003 ◽  
Vol 89 (6) ◽  
pp. 2889-2903 ◽  
Author(s):  
G. Christopher Stecker ◽  
Brian J. Mickey ◽  
Ewan A. Macpherson ◽  
John C. Middlebrooks
Keyword(s):  
Auditory Cortex ◽  
Primary Auditory Cortex ◽  
Median Plane ◽  
Stimulus Location ◽  
Population Coding ◽  
Broadband Noise ◽  
Spatial Sensitivity ◽  
Neural Ensembles ◽  
Artificial Neural Network Ann ◽  
Spike Patterns

We compared the spatial tuning properties of neurons in two fields [primary auditory cortex (A1) and posterior auditory field (PAF)] of cat auditory cortex. Broadband noise bursts of 80-ms duration were presented from loudspeakers throughout 360° in the horizontal plane (azimuth) or 260° in the vertical median plane (elevation). Sound levels varied from 20 to 40 dB above units' thresholds. We recorded neural spike activity simultaneously from 16 sites in field PAF and/or A1 of α-chloralose-anesthetized cats. We assessed spatial sensitivity by examining the dependence of spike count and response latency on stimulus location. In addition, we used an artificial neural network (ANN) to assess the information about stimulus location carried by spike patterns of single units and of ensembles of 2–32 units. The results indicate increased spatial sensitivity, more uniform distributions of preferred locations, and greater tolerance to changes in stimulus intensity among PAF units relative to A1 units. Compared to A1 units, PAF units responded at significantly longer latencies, and latencies varied more strongly with stimulus location. ANN analysis revealed significantly greater information transmission by spike patterns of PAF than A1 units, primarily reflecting the information transmitted by latency variation in PAF. Finally, information rates grew more rapidly with the number of units included in neural ensembles for PAF than A1. The latter finding suggests more accurate population coding of space in PAF, made possible by a more diverse population of neural response types.

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