Spectrotemporal receptive fields in anesthetized cat primary auditory cortex are context dependent

Decoding contextual influences on auditory perception from primary auditory cortex

Perception can be highly dependent on stimulus context, but whether and how sensory areas encode the context remains uncertain. We used an ambiguous auditory stimulus - a tritone pair - to investigate the neural activity associated with a preceding contextual stimulus that strongly influenced the tritone pair's perception: either as an ascending or a descending step in pitch. We recorded single-unit responses from a population of auditory cortical cells in awake ferrets listening to the tritone pairs preceded by the contextual stimulus. We find that the responses adapt locally to the contextual stimulus, consistent with human MEG recordings from the auditory cortex under the same conditions. Decoding the population responses demonstrates that pitch-change selective cells are able to predict well the context-sensitive percept of the tritone pairs. Conversely, decoding the distances between the pitch representations predicts the opposite of the percept. The various percepts can be readily captured and explained by a neural model of cortical activity based on populations of adapting, pitch and pitch-direction selective cells, aligned with the neurophysiological responses. Together, these decoding and model results suggest that contextual influences on perception may well be already encoded at the level of the primary sensory cortices, reflecting basic neural response properties commonly found in these areas.

Visuospatial attention revamps cortical processing of sound amid audiovisual uncertainty

Selective attentional biases arising from one sensory modality manifest in others. The effects of visuospatial attention, important in visual object perception, are unclear in the auditory domain during audiovisual (AV) scene processing. We investigate temporal and spatial factors that underlie such transfer neurally. Auditory encoding of random tone pips in AV scenes was addressed via a temporal response function model (TRF) of participants' electroencephalogram (N = 30). The spatially uninformative pips were associated with spatially distributed visual contrast reversals ("flips"), through asynchronous probabilistic AV temporal onset distributions. Participants deployed visuospatial selection on these AV stimuli to perform a task. A late (~300 ms) cross-modal influence over the neural representation of pips was found in the original and a replication study (N = 21). Transfer depended on selected visual input being (i) presented during or shortly after a related sound, in relatively limited temporal distributions (<165 ms); (ii) positioned across limited (1:4) visual foreground to background ratios. Neural encoding of auditory input, as a function of visual input, was largest at visual foreground quadrant sectors and lowest at locations opposite to the target. The results indicate that ongoing neural representations of sounds incorporate visuospatial attributes for auditory stream segregation, as cross-modal transfer conveys information that specifies the identity of multisensory signals. A potential mechanism is by enhancing or recalibrating the tuning properties of the auditory populations that represent them as objects. The results account for the dynamic evolution under visual attention of multisensory integration, specifying critical latencies at which relevant cortical networks operate.

Context Sensitivity across Multiple Time scales with a Flexible Frequency Bandwidth

Everyday auditory streams are complex, including spectro-temporal content that varies at multiple timescales. Using EEG, we investigated the sensitivity of human auditory cortex to the content of past stimulation in unattended sequences of equiprobable tones. In 3 experiments including 82 participants overall, we found that neural responses measured at different latencies after stimulus onset were sensitive to frequency intervals computed over distinct timescales. Importantly, early responses were sensitive to a longer history of stimulation than later responses. To account for these results, we tested a model consisting of neural populations with frequency-specific but broad tuning that undergo adaptation with exponential recovery. We found that the coexistence of neural populations with distinct recovery rates can explain our results. Furthermore, the adaptation bandwidth of these populations depended on spectral context-it was wider when the stimulation sequence had a wider frequency range. Our results provide electrophysiological evidence as well as a possible mechanistic explanation for dynamic and multiscale context-dependent auditory processing in the human cortex.

Plasticity of Multidimensional Receptive Fields in Core Rat Auditory Cortex Directed by Sound Statistics

Sensory cortical neurons can nonlinearly integrate a wide range of inputs. The outcome of this nonlinear process can be approximated by more than one receptive field component or filter to characterize the ensuing stimulus preference. The functional properties of multidimensional filters are, however, not well understood. Here we estimated two spectrotemporal receptive fields (STRFs) per neuron using maximally informative dimension analysis. We compared their temporal and spectral modulation properties and determined the stimulus information captured by the two STRFs in core rat auditory cortical fields, primary auditory cortex (A1) and ventral auditory field (VAF). The first STRF is the dominant filter and acts as a sound feature detector in both fields. The second STRF is less feature specific, preferred lower modulations, and had less spike information compared to the first STRF. The information jointly captured by the two STRFs was larger than that captured by the sum of the individual STRFs, reflecting nonlinear interactions of two filters. This information gain was larger in A1. We next determined how the acoustic environment affects the structure and relationship of these two STRFs. Rats were exposed to moderate levels of spectrotemporally modulated noise during development. Noise exposure strongly altered the spectrotemporal preference of the first STRF in both cortical fields. The interaction between the two STRFs was reduced by noise exposure in A1 but not in VAF. The results reveal new functional distinctions between A1 and VAF indicating that (i) A1 has stronger interactions of the two STRFs than VAF, (ii) noise exposure diminishes modulation parameter representation contained in the noise more strongly for the first STRF in both fields, and (iii) plasticity induced by noise exposure can affect the strength of filter interactions in A1. Taken together, ascertaining two STRFs per neuron enhances the understanding of cortical information processing and plasticity effects in core auditory cortex.

Learning Speech Production and Perception through Sensorimotor Interactions

Action and perception are closely linked in many behaviors necessitating a close coordination between sensory and motor neural processes so as to achieve a well-integrated smoothly evolving task performance. To investigate the detailed nature of these sensorimotor interactions, and their role in learning and executing the skilled motor task of speaking, we analyzed ECoG recordings of responses in the high-γ band (70-150 Hz) in human subjects while they listened to, spoke, or silently articulated speech. We found elaborate spectrotemporally modulated neural activity projecting in both "forward" (motor-to-sensory) and "inverse" directions between the higher-auditory and motor cortical regions engaged during speaking. Furthermore, mathematical simulations demonstrate a key role for the forward projection in "learning" to control the vocal tract, beyond its commonly postulated predictive role during execution. These results therefore offer a broader view of the functional role of the ubiquitous forward projection as an important ingredient in learning, rather than just control, of skilled sensorimotor tasks.

Simple transformations capture auditory input to cortex

Sounds are processed by the ear and central auditory pathway. These processing steps are biologically complex, and many aspects of the transformation from sound waveforms to cortical response remain unclear. To understand this transformation, we combined models of the auditory periphery with various encoding models to predict auditory cortical responses to natural sounds. The cochlear models ranged from detailed biophysical simulations of the cochlea and auditory nerve to simple spectrogram-like approximations of the information processing in these structures. For three different stimulus sets, we tested the capacity of these models to predict the time course of single-unit neural responses recorded in ferret primary auditory cortex. We found that simple models based on a log-spaced spectrogram with approximately logarithmic compression perform similarly to the best-performing biophysically detailed models of the auditory periphery, and more consistently well over diverse natural and synthetic sounds. Furthermore, we demonstrated that including approximations of the three categories of auditory nerve fiber in these simple models can substantially improve prediction, particularly when combined with a network encoding model. Our findings imply that the properties of the auditory periphery and central pathway may together result in a simpler than expected functional transformation from ear to cortex. Thus, much of the detailed biological complexity seen in the auditory periphery does not appear to be important for understanding the cortical representation of sound.

A dynamic network model of temporal receptive fields in primary auditory cortex

Auditory neurons encode stimulus history, which is often modelled using a span of time-delays in a spectro-temporal receptive field (STRF). We propose an alternative model for the encoding of stimulus history, which we apply to extracellular recordings of neurons in the primary auditory cortex of anaesthetized ferrets. For a linear-non-linear STRF model (LN model) to achieve a high level of performance in predicting single unit neural responses to natural sounds in the primary auditory cortex, we found that it is necessary to include time delays going back at least 200 ms in the past. This is an unrealistic time span for biological delay lines. We therefore asked how much of this dependence on stimulus history can instead be explained by dynamical aspects of neurons. We constructed a neural-network model whose output is the weighted sum of units whose responses are determined by a dynamic firing-rate equation. The dynamic aspect performs low-pass filtering on each unit's response, providing an exponentially decaying memory whose time constant is individual to each unit. We find that this dynamic network (DNet) model, when fitted to the neural data using STRFs of only 25 ms duration, can achieve prediction performance on a held-out dataset comparable to the best performing LN model with STRFs of 200 ms duration. These findings suggest that integration due to the membrane time constants or other exponentially-decaying memory processes may underlie linear temporal receptive fields of neurons beyond 25 ms.

STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds

We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.

The information carried by natural sounds enters the cortex of mammals in a specific format: the cochleagram. Instead of representing the original pressure waveforms, the inner ear represents how the energy in a sound is distributed across frequency bands and how the energy distribution evolves over time. The generation of cochleagrams is highly non-linear resulting in the dominance of one sound source per time-frequency bin under natural conditions (masking). Auditory cortex is believed to decompose cochleagrams into structural primitives, i.e., reappearing regular spectro-temporal subpatterns that make up cochleagram patterns (similar to edges in images). However, such a decomposition has so far only been modeled without considering masking and non-negativity. Here we apply a novel non-linear sparse coding model that can capture masking non-linearities and non-negativities. When trained on cochleagrams of natural sounds, the model gives rise to an encoding primarily based-on spectro-temporally localized components. If stimulated by a sound, the encoding units compete to explain its contents. The competition is a direct consequence of the statistical sound model, and it results in neural responses being best described by spectro-temporal receptive fields (STRFs) with positive and negative subfields. The emerging STRFs show a higher similarity to experimentally measured STRFs than a model without masking, which provides evidence for cortical encoding being consistent with the masking based sound statistics of cochleagrams. Furthermore, and more generally, our study suggests for the first time that negative subfields of STRFs may be direct evidence for explaining away effects resulting from performing inference in an underlying statistical model.

Multidimensional receptive field processing by cat primary auditory cortical neurons

The receptive fields of many auditory cortical neurons are multidimensional and are best represented by more than one stimulus feature. The number of these dimensions, their characteristics, and how they differ with stimulus context have been relatively unexplored. Standard methods that are often used to characterize multidimensional stimulus selectivity, such as spike-triggered covariance (STC) or maximally informative dimensions (MIDs), are either limited to Gaussian stimuli or are only able to recover a small number of stimulus features due to data limitations. An information theoretic extension of STC, the maximum noise entropy (MNE) model, can be used with non-Gaussian stimulus distributions to find an arbitrary number of stimulus dimensions. When we applied the MNE model to auditory cortical neurons, we often found more than two stimulus features that influenced neuronal firing. Excitatory and suppressive features coded different acoustic contexts: excitatory features encoded higher temporal and spectral modulations, while suppressive features had lower modulation frequency preferences. We found that the excitatory and suppressive features themselves were sensitive to stimulus context when we employed two stimuli that differed only in their short-term correlation structure: while the linear features were similar, the secondary features were strongly affected by stimulus statistics. These results show that multidimensional receptive field processing is influenced by feature type and stimulus context.

Long-Term Impairment of Sound Processing in the Auditory Midbrain by Daily Short-Term Exposure to Moderate Noise

Most citizen people are exposed daily to environmental noise at moderate levels with a short duration. The aim of the present study was to determine the effects of daily short-term exposure to moderate noise on sound level processing in the auditory midbrain. Sound processing properties of auditory midbrain neurons were recorded in anesthetized mice exposed to moderate noise (80 dB SPL, 2 h/d for 6 weeks) and were compared with those from age-matched controls. Neurons in exposed mice had a higher minimum threshold and maximum response intensity, a longer first spike latency, and a higher slope and narrower dynamic range for rate level function. However, these observed changes were greater in neurons with the best frequency within the noise exposure frequency range compared with those outside the frequency range. These sound processing properties also remained abnormal after a 12-week period of recovery in a quiet laboratory environment after completion of noise exposure. In conclusion, even daily short-term exposure to moderate noise can cause long-term impairment of sound level processing in a frequency-specific manner in auditory midbrain neurons.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

Capturing contextual effects in spectro-temporal receptive fields

Spectro-temporal receptive fields (STRFs) are thought to provide descriptive images of the computations performed by neurons along the auditory pathway. However, their validity can be questioned because they rely on a set of assumptions that are probably not fulfilled by real neurons exhibiting contextual effects, that is, nonlinear interactions in the time or frequency dimension that cannot be described with a linear filter. We used a novel approach to investigate how a variety of contextual effects, due to facilitating nonlinear interactions and synaptic depression, affect different STRF models, and if these effects can be captured with a context field (CF). Contextual effects were incorporated in simulated networks of spiking neurons, allowing one to define the true STRFs of the neurons. This, in turn, made it possible to evaluate the performance of each STRF model by comparing the estimations with the true STRFs. We found that currently used STRF models are particularly poor at estimating inhibitory regions. Specifically, contextual effects make estimated STRFs dependent on stimulus density in a contrasting fashion: inhibitory regions are underestimated at lower densities while artificial inhibitory regions emerge at higher densities. The CF was found to provide a solution to this dilemma, but only when it is used together with a generalized linear model. Our results therefore highlight the limitations of the traditional STRF approach and provide useful recipes for how different STRF models and stimuli can be used to arrive at reliable quantifications of neural computations in the presence of contextual effects. The results therefore push the purpose of STRF analysis from simply finding an optimal stimulus toward describing context-dependent computations of neurons along the auditory pathway.

Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds

Sensory neurons are customarily characterized by one or more linearly weighted receptive fields describing sensitivity in sensory space and time. We show that in auditory cortical and thalamic neurons, the weight of each receptive field element depends on the pattern of sound falling within a local neighborhood surrounding it in time and frequency. Accounting for this change in effective receptive field with spectrotemporal context improves predictions of both cortical and thalamic responses to stationary complex sounds. Although context dependence varies among neurons and across brain areas, there are strong shared qualitative characteristics. In a spectrotemporally rich soundscape, sound elements modulate neuronal responsiveness more effectively when they coincide with sounds at other frequencies, and less effectively when they are preceded by sounds at similar frequencies. This local-context-driven lability in the representation of complex sounds—a modulation of “input-specific gain” rather than “output gain”—may be a widespread motif in sensory processing.

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Gain of neuronal responses to sound components varies with immediate acoustic context

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“Contextual gain fields” can be estimated from neuronal responses to complex sounds

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Coincident sound at different frequencies boosts gain in cortex and thalamus

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Preceding sound at similar frequency reduces gain for longer in cortex than thalamus

Stable encoding of sounds over a broad range of statistical parameters in the auditory cortex

Natural auditory scenes possess highly structured statistical regularities, which are dictated by the physics of sound production in nature, such as scale‐invariance. We recently identified that natural water sounds exhibit a particular type of scale invariance, in which the temporal modulation within spectral bands scales with the centre frequency of the band. Here, we tested how neurons in the mammalian primary auditory cortex encode sounds that exhibit this property, but differ in their statistical parameters. The stimuli varied in spectro‐temporal density and cyclo‐temporal statistics over several orders of magnitude, corresponding to a range of water‐like percepts, from pattering of rain to a slow stream. We recorded neuronal activity in the primary auditory cortex of awake rats presented with these stimuli. The responses of the majority of individual neurons were selective for a subset of stimuli with specific statistics. However, as a neuronal population, the responses were remarkably stable over large changes in stimulus statistics, exhibiting a similar range in firing rate, response strength, variability and information rate, and only minor variation in receptive field parameters. This pattern of neuronal responses suggests a potentially general principle for cortical encoding of complex acoustic scenes: while individual cortical neurons exhibit selectivity for specific statistical features, a neuronal population preserves a constant response structure across a broad range of statistical parameters.

Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear-nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately.

SIGNIFICANCE STATEMENT

An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it introduces no new free parameters. Incorporating the adaptive coding properties of neurons will likely improve receptive field models in other sensory modalities too.

Frequency-specific adaptation and its underlying circuit model in the auditory midbrain

Receptive fields of sensory neurons are considered to be dynamic and depend on the stimulus history. In the auditory system, evidence of dynamic frequency-receptive fields has been found following stimulus-specific adaptation (SSA). However, the underlying mechanism and circuitry of SSA have not been fully elucidated. Here, we studied how frequency-receptive fields of neurons in rat inferior colliculus (IC) changed when exposed to a biased tone sequence. Pure tone with one specific frequency (adaptor) was presented markedly more often than others. The adapted tuning was compared with the original tuning measured with an unbiased sequence. We found inhomogeneous changes in frequency tuning in IC, exhibiting a center-surround pattern with respect to the neuron's best frequency. Central adaptors elicited strong suppressive and repulsive changes while flank adaptors induced facilitative and attractive changes. Moreover, we proposed a two-layer model of the underlying network, which not only reproduced the adaptive changes in the receptive fields but also predicted novelty responses to oddball sequences. These results suggest that frequency-specific adaptation in auditory midbrain can be accounted for by an adapted frequency channel and its lateral spreading of adaptation, which shed light on the organization of the underlying circuitry.

One hundred ways to process time, frequency, rate and scale in the central auditory system: a pattern-recognition meta-analysis

The mammalian auditory system extracts features from the acoustic environment based on the responses of spatially distributed sets of neurons in the subcortical and cortical auditory structures. The characteristic responses of these neurons (linearly approximated by their spectro-temporal receptive fields, or STRFs) suggest that auditory representations are formed, as early as in the inferior colliculi, on the basis of a time, frequency, rate (temporal modulations) and scale (spectral modulations) analysis of sound. However, how these four dimensions are integrated and processed in subsequent neural networks remains unclear. In this work, we present a new methodology to generate computational insights into the functional organization of such processes. We first propose a systematic framework to explore more than a hundred different computational strategies proposed in the literature to process the output of a generic STRF model. We then evaluate these strategies on their ability to compute perceptual distances between pairs of environmental sounds. Finally, we conduct a meta-analysis of the dataset of all these algorithms' accuracies to examine whether certain combinations of dimensions and certain ways to treat such dimensions are, on the whole, more computationally effective than others. We present an application of this methodology to a dataset of ten environmental sound categories, in which the analysis reveals that (1) models are most effective when they organize STRF data into frequency groupings—which is consistent with the known tonotopic organization of receptive fields in auditory structures -, and that (2) models that treat STRF data as time series are no more effective than models that rely only on summary statistics along time—which corroborates recent experimental evidence on texture discrimination by summary statistics.

Context-dependent coding and gain control in the auditory system of crickets

Sensory systems process stimuli that greatly vary in intensity and complexity. To maintain efficient information transmission, neural systems need to adjust their properties to these different sensory contexts, yielding adaptive or stimulus-dependent codes. Here, we demonstrated adaptive spectrotemporal tuning in a small neural network, i.e. the peripheral auditory system of the cricket. We found that tuning of cricket auditory neurons was sharper for complex multi-band than for simple single-band stimuli. Information theoretical considerations revealed that this sharpening improved information transmission by separating the neural representations of individual stimulus components. A network model inspired by the structure of the cricket auditory system suggested two putative mechanisms underlying this adaptive tuning: a saturating peripheral nonlinearity could change the spectral tuning, whereas broad feed-forward inhibition was able to reproduce the observed adaptive sharpening of temporal tuning. Our study revealed a surprisingly dynamic code usually found in more complex nervous systems and suggested that stimulus-dependent codes could be implemented using common neural computations.

Dynamic representation of spectral edges in guinea pig primary auditory cortex

The central representation of a given acoustic motif is thought to be strongly context dependent, i.e., to rely on the spectrotemporal past and present of the acoustic mixture in which it is embedded. The present study investigated the cortical representation of spectral edges (i.e., where stimulus energy changes abruptly over frequency) and its dependence on stimulus duration and depth of the spectral contrast in guinea pig. We devised a stimulus ensemble composed of random tone pips with or without an attenuated frequency band (AFB) of variable depth. Additionally, the multitone ensemble with AFB was interleaved with periods of silence or with multitone ensembles without AFB. We have shown that the representation of the frequencies near but outside the AFB is greatly enhanced, whereas the representation of frequencies near and inside the AFB is strongly suppressed. These cortical changes depend on the depth of the AFB: although they are maximal for the largest depth of the AFB, they are also statistically significant for depths as small as 10 dB. Finally, the cortical changes are quick, occurring within a few seconds of stimulus ensemble presentation with AFB, and are very labile, disappearing within a few seconds after the presentation without AFB. Overall, this study demonstrates that the representation of spectral edges is dynamically enhanced in the auditory centers. These central changes may have important functional implications, particularly in noisy environments where they could contribute to preserving the central representation of spectral edges.

Spectrotemporal response properties of core auditory cortex neurons in awake monkey

So far, most studies of core auditory cortex (AC) have characterized the spectral and temporal tuning properties of cells in non-awake, anesthetized preparations. As experiments in awake animals are scarce, we here used dynamic spectral-temporal broadband ripples to study the properties of the spectrotemporal receptive fields (STRFs) of AC cells in awake monkeys. We show that AC neurons were typically most sensitive to low ripple densities (spectral) and low velocities (temporal), and that most cells were not selective for a particular spectrotemporal sweep direction. A substantial proportion of neurons preferred amplitude-modulated sounds (at zero ripple density) to dynamic ripples (at non-zero densities). The vast majority (>93%) of modulation transfer functions were separable with respect to spectral and temporal modulations, indicating that time and spectrum are independently processed in AC neurons. We also analyzed the linear predictability of AC responses to natural vocalizations on the basis of the STRF. We discuss our findings in the light of results obtained from the monkey midbrain inferior colliculus by comparing the spectrotemporal tuning properties and linear predictability of these two important auditory stages.

A new and fast characterization of multiple encoding properties of auditory neurons

The functional properties of auditory cortex neurons are most often investigated separately, through spectrotemporal receptive fields (STRFs) for the frequency tuning and the use of frequency sweeps sounds for selectivity to velocity and direction. In fact, auditory neurons are sensitive to a multidimensional space of acoustic parameters where spectral, temporal and spatial dimensions interact. We designed a multi-parameter stimulus, the random double sweep (RDS), composed of two uncorrelated random sweeps, which gives an easy, fast and simultaneous access to frequency tuning as well as frequency modulation sweep direction and velocity selectivity, frequency interactions and temporal properties of neurons. Reverse correlation techniques applied to recordings from the primary auditory cortex of guinea pigs and rats in response to RDS stimulation revealed the variety of temporal dynamics of acoustic patterns evoking an enhanced or suppressed firing rate. Group results on these two species revealed less frequent suppression areas in frequency tuning STRFs, the absence of downward sweep selectivity, and lower phase locking abilities in the auditory cortex of rats compared to guinea pigs.

Enhanced representation of spectral contrasts in the primary auditory cortex

The role of early auditory processing may be to extract some elementary features from an acoustic mixture in order to organize the auditory scene. To accomplish this task, the central auditory system may rely on the fact that sensory objects are often composed of spectral edges, i.e., regions where the stimulus energy changes abruptly over frequency. The processing of acoustic stimuli may benefit from a mechanism enhancing the internal representation of spectral edges. While the visual system is thought to rely heavily on this mechanism (enhancing spatial edges), it is still unclear whether a related process plays a significant role in audition. We investigated the cortical representation of spectral edges, using acoustic stimuli composed of multi-tone pips whose time-averaged spectral envelope contained suppressed or enhanced regions. Importantly, the stimuli were designed such that neural responses properties could be assessed as a function of stimulus frequency during stimulus presentation. Our results suggest that the representation of acoustic spectral edges is enhanced in the auditory cortex, and that this enhancement is sensitive to the characteristics of the spectral contrast profile, such as depth, sharpness and width. Spectral edges are maximally enhanced for sharp contrast and large depth. Cortical activity was also suppressed at frequencies within the suppressed region. To note, the suppression of firing was larger at frequencies nearby the lower edge of the suppressed region than at the upper edge. Overall, the present study gives critical insights into the processing of spectral contrasts in the auditory system.

Pre-attentive, context-specific representation of fear memory in the auditory cortex of rat

Neural representation in the auditory cortex is rapidly modulated by both top-down attention and bottom-up stimulus properties, in order to improve perception in a given context. Learning-induced, pre-attentive, map plasticity has been also studied in the anesthetized cortex; however, little attention has been paid to rapid, context-dependent modulation. We hypothesize that context-specific learning leads to pre-attentively modulated, multiplex representation in the auditory cortex. Here, we investigate map plasticity in the auditory cortices of anesthetized rats conditioned in a context-dependent manner, such that a conditioned stimulus (CS) of a 20-kHz tone and an unconditioned stimulus (US) of a mild electrical shock were associated only under a noisy auditory context, but not in silence. After the conditioning, although no distinct plasticity was found in the tonotopic map, tone-evoked responses were more noise-resistive than pre-conditioning. Yet, the conditioned group showed a reduced spread of activation to each tone with noise, but not with silence, associated with a sharpening of frequency tuning. The encoding accuracy index of neurons showed that conditioning deteriorated the accuracy of tone-frequency representations in noisy condition at off-CS regions, but not at CS regions, suggesting that arbitrary tones around the frequency of the CS were more likely perceived as the CS in a specific context, where CS was associated with US. These results together demonstrate that learning-induced plasticity in the auditory cortex occurs in a context-dependent manner.

How do auditory cortex neurons represent communication sounds?

A major goal in auditory neuroscience is to characterize how communication sounds are represented at the cortical level. The present review aims at investigating the role of auditory cortex in the processing of speech, bird songs and other vocalizations, which all are spectrally and temporally highly structured sounds. Whereas earlier studies have simply looked for neurons exhibiting higher firing rates to particular conspecific vocalizations over their modified, artificially synthesized versions, more recent studies determined the coding capacity of temporal spike patterns, which are prominent in primary and non-primary areas (and also in non-auditory cortical areas). In several cases, this information seems to be correlated with the behavioral performance of human or animal subjects, suggesting that spike-timing based coding strategies might set the foundations of our perceptive abilities. Also, it is now clear that the responses of auditory cortex neurons are highly nonlinear and that their responses to natural stimuli cannot be predicted from their responses to artificial stimuli such as moving ripples and broadband noises. Since auditory cortex neurons cannot follow rapid fluctuations of the vocalizations envelope, they only respond at specific time points during communication sounds, which can serve as temporal markers for integrating the temporal and spectral processing taking place at subcortical relays. Thus, the temporal sparse code of auditory cortex neurons can be considered as a first step for generating high level representations of communication sounds independent of the acoustic characteristic of these sounds. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives".

Differences between spectro-temporal receptive fields derived from artificial and natural stimuli in the auditory cortex

Spectro-temporal properties of auditory cortex neurons have been extensively studied with artificial sounds but it is still unclear whether they help in understanding neuronal responses to communication sounds. Here, we directly compared spectro-temporal receptive fields (STRFs) obtained from the same neurons using both artificial stimuli (dynamic moving ripples, DMRs) and natural stimuli (conspecific vocalizations) that were matched in terms of spectral content, average power and modulation spectrum. On a population of auditory cortex neurons exhibiting reliable tuning curves when tested with pure tones, significant STRFs were obtained for 62% of the cells with vocalizations and 68% with DMR. However, for many cells with significant vocalization-derived STRFs (STRF_voc) and DMR-derived STRFs (STRF_dmr), the BF, latency, bandwidth and global STRFs shape differed more than what would be predicted by spiking responses simulated by a linear model based on a non-homogenous Poisson process. Moreover STRF_voc predicted neural responses to vocalizations more accurately than STRF_dmr predicted neural response to DMRs, despite similar spike-timing reliability for both sets of stimuli. Cortical bursts, which potentially introduce nonlinearities in evoked responses, did not explain the differences between STRF_voc and STRF_dmr. Altogether, these results suggest that the nonlinearity of auditory cortical responses makes it difficult to predict responses to communication sounds from STRFs computed from artificial stimuli.

Spectrotemporal contrast kernels for neurons in primary auditory cortex

Auditory neurons are often described in terms of their spectrotemporal receptive fields (STRFs). These map the relationship between features of the sound spectrogram and firing rates of neurons. Recently, we showed that neurons in the primary fields of the ferret auditory cortex are also subject to gain control: when sounds undergo smaller fluctuations in their level over time, the neurons become more sensitive to small-level changes (Rabinowitz et al., 2011). Just as STRFs measure the spectrotemporal features of a sound that lead to changes in the firing rates of neurons, in this study, we sought to estimate the spectrotemporal regions in which sound statistics lead to changes in the gain of neurons. We designed a set of stimuli with complex contrast profiles to characterize these regions. This allowed us to estimate the STRFs of cortical neurons alongside a set of spectrotemporal contrast kernels. We find that these two sets of integration windows match up: the extent to which a stimulus feature causes the firing rate of a neuron to change is strongly correlated with the extent to which the contrast of that feature modulates the gain of the neuron. Adding contrast kernels to STRF models also yields considerable improvements in the ability to capture and predict how auditory cortical neurons respond to statistically complex sounds.

Extra-classical tuning predicts stimulus-dependent receptive fields in auditory neurons

The receptive fields of many sensory neurons are sensitive to statistical differences among classes of complex stimuli. For example, excitatory spectral bandwidths of midbrain auditory neurons and the spatial extent of cortical visual neurons differ during the processing of natural stimuli compared to the processing of artificial stimuli. Experimentally characterizing neuronal nonlinearities that contribute to stimulus-dependent receptive fields is important for understanding how neurons respond to different stimulus classes in multiple sensory modalities. Here we show that in the zebra finch, many auditory midbrain neurons have extra-classical receptive fields, consisting of sideband excitation and sideband inhibition. We also show that the presence, degree, and asymmetry of stimulus-dependent receptive fields during the processing of complex sounds are predicted by the presence, valence, and asymmetry of extra-classical tuning. Neurons for which excitatory bandwidth expands during the processing of song have extra-classical excitation. Neurons for which frequency tuning is static and for which excitatory bandwidth contracts during the processing of song have extra-classical inhibition. Simulation experiments further demonstrate that stimulus-dependent receptive fields can arise from extra-classical tuning with a static spike threshold nonlinearity. These findings demonstrate that a common neuronal nonlinearity can account for the stimulus dependence of receptive fields estimated from the responses of auditory neurons to stimuli with natural and non-natural statistics.

Neuron-specific stimulus masking reveals interference in spike timing at the cortical level

The auditory system is capable of robust recognition of sounds in the presence of competing maskers (e.g., other voices or background music). This capability arises despite the fact that masking stimuli can disrupt neural responses at the cortical level. Since the origins of such interference effects remain unknown, in this study, we work to identify and quantify neural interference effects that originate due to masking occurring within and outside receptive fields of neurons. We record from single and multi-unit auditory sites from field L, the auditory cortex homologue in zebra finches. We use a novel method called spike timing-based stimulus filtering that uses the measured response of each neuron to create an individualized stimulus set. In contrast to previous adaptive experimental approaches, which have typically focused on the average firing rate, this method uses the complete pattern of neural responses, including spike timing information, in the calculation of the receptive field. When we generate and present novel stimuli for each neuron that mask the regions within the receptive field, we find that the time-varying information in the neural responses is disrupted, degrading neural discrimination performance and decreasing spike timing reliability and sparseness. We also find that, while removing stimulus energy from frequency regions outside the receptive field does not significantly affect neural responses for many sites, adding a masker in these frequency regions can nonetheless have a significant impact on neural responses and discriminability without a significant change in the average firing rate. These findings suggest that maskers can interfere with neural responses by disrupting stimulus timing information with power either within or outside the receptive fields of neurons.

Modeling frequency modulated responses of midbrain auditory neurons based on trigger features and artificial neural networks

Frequency modulation (FM) is an important building block of communication signals for animals and human. Attempts to predict the response of central neurons to FM sounds have not been very successful, though achieving successful results could bring insights regarding the underlying neural mechanisms. Here we proposed a new method to predict responses of FM-sensitive neurons in the auditory midbrain. First we recorded single unit responses in anesthetized rats using a random FM tone to construct their spectro-temporal receptive fields (STRFs). Training of neurons in the artificial neural network to respond to a second random FM tone was based on the temporal information derived from the STRF. Specifically, the time window covered by the presumed trigger feature and its delay time to spike occurrence were used to train a finite impulse response neural network (FIRNN) to respond to this random FM. Finally we tested the model performance in predicting the response to another similar FM stimuli (third random FM tone). We found good performance in predicting the time of responses if not also the response magnitudes. Furthermore, the weighting function of the FIRNN showed temporal 'bumps' suggesting temporal integration of synaptic inputs from different frequency laminae. This article is part of a Special Issue entitled: Neural Coding.

How different are the local field potentials and spiking activities? Insights from multi-electrodes arrays

Simultaneous recording of multiple neurons, or neuron groups, offers new promise for investigating fundamental questions about the neural code. We used arrays of 16 electrodes in the tonotopic, primary, auditory cortex of guinea pigs and we extracted LFP- and spike-based spectro-temporal receptive fields (STRFs). We confirm here that LFP signals provide broadly tuned activity which lacks frequency resolution compared to multiunit signals and, therefore, lead to large redundancy in neural responses even between recording sites far apart. Thanks to the use of multi-electrode arrays which allows simultaneous recordings, we also focused on functional relationships between neuronal discharges (through cross-correlations) and between LFPs (through coherence). Since the LFP is composed of distinct brain rhythms, the LFP results were split into three frequency bands from the slowest to the fastest components of LFPs. For driven as well as spontaneous activity, we show that components >70 Hz in LFPs are much less coherent between recording sites than slower components. In general, coherence between LFPs from two recordings sites is positively correlated with the degree of frequency overlap between the two corresponding STRFs, similar to cross-correlation between multiunit activities. However, coherence is only weakly correlated with cross-correlation in all frequency ranges. Altogether, these results suggest that LFPs reflect global functional connectivity in the thalamocortical auditory system whereas spiking activities reflect more independent local processing.

Understanding auditory spectro-temporal receptive fields and their changes with input statistics by efficient coding principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed.

Contrast gain control in auditory cortex

The auditory system must represent sounds with a wide range of statistical properties. One important property is the spectrotemporal contrast in the acoustic environment: the variation in sound pressure in each frequency band, relative to the mean pressure. We show that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation. When contrast is low, neurons increase their gain, becoming more sensitive to small changes in the stimulus, although the effectiveness of contrast gain control is reduced at low mean levels. Gain is primarily determined by contrast near each neuron's preferred frequency, but there is also a contribution from contrast in more distant frequency bands. Neural responses are modulated by contrast over timescales of ∼100 ms. By using contrast gain control to expand or compress the representation of its inputs, the auditory system may be seeking an efficient coding of natural sounds.

Comparison of LFP-based and spike-based spectro-temporal receptive fields and cross-correlation in cat primary auditory cortex

Multi-electrode array recordings of spike and local field potential (LFP) activity were made from primary auditory cortex of 12 normal hearing, ketamine-anesthetized cats. We evaluated 259 spectro-temporal receptive fields (STRFs) and 492 frequency-tuning curves (FTCs) based on LFPs and spikes simultaneously recorded on the same electrode. We compared their characteristic frequency (CF) gradients and their cross-correlation distances. The CF gradient for spike-based FTCs was about twice that for 2–40 Hz-filtered LFP-based FTCs, indicating greatly reduced frequency selectivity for LFPs. We also present comparisons for LFPs band-pass filtered between 4–8 Hz, 8–16 Hz and 16–40 Hz, with spike-based STRFs, on the basis of their marginal frequency distributions. We find on average a significantly larger correlation between the spike based marginal frequency distributions and those based on the 16–40 Hz filtered LFP, compared to those based on the 4–8 Hz, 8–16 Hz and 2–40 Hz filtered LFP. This suggests greater frequency specificity for the 16–40 Hz LFPs compared to those of lower frequency content. For spontaneous LFP and spike activity we evaluated 1373 pair correlations for pairs with >200 spikes in 900 s per electrode. Peak correlation-coefficient space constants were similar for the 2–40 Hz filtered LFP (5.5 mm) and the 16–40 Hz LFP (7.4 mm), whereas for spike-pair correlations it was about half that, at 3.2 mm. Comparing spike-pairs with 2–40 Hz (and 16–40 Hz) LFP-pair correlations showed that about 16% (9%) of the variance in the spike-pair correlations could be explained from LFP-pair correlations recorded on the same electrodes within the same electrode array. This larger correlation distance combined with the reduced CF gradient and much broader frequency selectivity suggests that LFPs are not a substitute for spike activity in primary auditory cortex.

Complex spectral interactions encoded by auditory cortical neurons: relationship between bandwidth and pattern

The focus of most research on auditory cortical neurons has concerned the effects of rather simple stimuli, such as pure tones or broad-band noise, or the modulation of a single acoustic parameter. Extending these findings to feature coding in more complex stimuli such as natural sounds may be difficult, however. Generalizing results from the simple to more complex case may be complicated by non-linear interactions occurring between multiple, simultaneously varying acoustic parameters in complex sounds. To examine this issue in the frequency domain, we performed a parametric study of the effects of two global features, spectral pattern (here ripple frequency) and bandwidth, on primary auditory (A1) neurons in awake macaques. Most neurons were tuned for one or both variables and most also displayed an interaction between bandwidth and pattern implying that their effects were conditional or interdependent. A spectral linear filter model was able to qualitatively reproduce the basic effects and interactions, indicating that a simple neural mechanism may be able to account for these interdependencies. Our results suggest that the behavior of most A1 neurons is likely to depend on multiple parameters, and so most are unlikely to respond independently or invariantly to specific acoustic features.

Context dependence of spectro-temporal receptive fields with implications for neural coding

The spectro-temporal receptive field (STRF) is frequently used to characterize the linear frequency-time filter properties of the auditory system up to the neuron recorded from. STRFs are extremely stimulus dependent, reflecting the strong non-linearities in the auditory system. Changes in the STRF with stimulus type (tonal, noise-like, vocalizations), sound level and spectro-temporal sound density are reviewed here. Effects on STRF shape of task and attention are also briefly reviewed. Models to account for these changes, potential improvements to STRF analysis, and implications for neural coding are discussed.

Nonlinear cross-frequency interactions in primary auditory cortex spectrotemporal receptive fields: a Wiener-Volterra analysis

The effects of nonlinear interactions between different sound frequencies on the responses of neurons in primary auditory cortex (AI) have only been investigated using two-tone paradigms. Here we stimulated with relatively dense, Poisson-distributed trains of tone pips (with frequency ranges spanning five octaves, 16 frequencies /octave, and mean rates of 20 or 120 pips /s), and examined within-frequency (or auto-frequency) and cross-frequency interactions in three types of AI unit responses by computing second-order "Poisson-Wiener" auto- and cross-kernels. Units were classified on the basis of their spectrotemporal receptive fields (STRFs) as "double-peaked", "single-peaked" or "peak-valley". Second-order interactions were investigated between the two bands of excitatory frequencies on double-peaked STRFs, between an excitatory band and various non-excitatory bands on single-peaked STRFs, and between an excitatory band and an inhibitory sideband on peak-valley STRFs. We found that auto-frequency interactions (i.e., those within a single excitatory band) were always characterized by a strong depression of (first-order) excitation that decayed with the interstimulus lag up to approximately 200 ms. That depression was weaker in cross-frequency compared to auto-frequency interactions for approximately 25% of dual-peaked STRFs, evidence of "combination sensitivity" for the two bands. Non-excitatory and inhibitory frequencies (on single-peaked and peak-valley STRFs, respectively) typically weakly depressed the excitatory response at short interstimulus lags (<50 ms), but weakly facilitated it at longer lags ( approximately 50-200 ms). Both the depression and especially the facilitation were stronger for interactions with inhibitory frequencies rather than just non-excitatory ones. Finally, facilitation in single-peaked and peak-valley units decreased with increasing stimulus density. Our results indicate that the strong combination sensitivity and cross-frequency facilitation suggested by previous two-tone-paradigm studies are much less pronounced when using more temporally-dense stimuli.

Effects of adaptation on spectrotemporal receptive fields in primary auditory cortex

Spectrotemporal receptive fields of nonlinear neurons in primary auditory cortex are stimulus dependent or context dependent. Here we show that a variant of stimulus-specific adaptation also contributes to this context dependence. Responses to sound stimulus frequencies close to the neuron's best frequency adapt with an average time constant of approximately 7 s. In contrast, responses away from the best frequency do not adapt, but in fact slightly increase over our 30-s observation window. Such stimulus-specific adaptation could function in enhancing stimulus discrimination and in maximizing neural information transmission by reducing redundancy. It also needs to be taken into account when comparing spectrotemporal receptive fields measured under adapted and nonadapted conditions.

Rapid synaptic depression explains nonlinear modulation of spectro-temporal tuning in primary auditory cortex by natural stimuli

In this study, we explored ways to account more accurately for responses of neurons in primary auditory cortex (A1) to natural sounds. The auditory cortex has evolved to extract behaviorally relevant information from complex natural sounds, but most of our understanding of its function is derived from experiments using simple synthetic stimuli. Previous neurophysiological studies have found that existing models, such as the linear spectro-temporal receptive field (STRF), fail to capture the entire functional relationship between natural stimuli and neural responses. To study this problem, we compared STRFs for A1 neurons estimated using a natural stimulus, continuous speech, with STRFs estimated using synthetic ripple noise. For about one-third of the neurons, we found significant differences between STRFs, usually in the temporal dynamics of inhibition and/or overall gain. This shift in tuning resulted primarily from differences in the coarse temporal structure of the speech and noise stimuli. Using simulations, we found that the stimulus dependence of spectro-temporal tuning can be explained by a model in which synaptic inputs to A1 neurons are susceptible to rapid nonlinear depression. This dynamic reshaping of spectro-temporal tuning suggests that synaptic depression may enable efficient encoding of natural auditory stimuli.