Analysis of dynamic spectra in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary dynamic spectra

Composite receptive fields in the mouse auditory cortex

A central question in sensory neuroscience is how neurons represent complex natural stimuli. This process involves multiple steps of feature extraction to obtain a condensed, categorical representation useful for classification and behaviour. It has previously been shown that central auditory neurons in the starling have composite receptive fields composed of multiple features. Whether this property is an idiosyncratic characteristic of songbirds, a group of highly specialized vocal learners or a generic property of sensory processing is unknown. To address this question, we have recorded responses from auditory cortical neurons in mice, and characterized their receptive fields using mouse ultrasonic vocalizations (USVs) as a natural and ethologically relevant stimulus and pitch-shifted starling songs as a natural but ethologically irrelevant control stimulus. We have found that these neurons display composite receptive fields with multiple excitatory and inhibitory subunits. Moreover, this was the case with either the conspecific or the heterospecific vocalizations. We then trained the sparse filtering algorithm on both classes of natural stimuli to obtain statistically optimal features, and compared the natural and artificial features using UMAP, a dimensionality-reduction algorithm previously used to analyse mouse USVs and birdsongs. We have found that the receptive-field features obtained with both types of the natural stimuli clustered together, as did the sparse-filtering features. However, the natural and artificial receptive-field features clustered mostly separately. Based on these results, our general conclusion is that composite receptive fields are not a unique characteristic of specialized vocal learners but are likely a generic property of central auditory systems. KEY POINTS: Auditory cortical neurons in the mouse have composite receptive fields with several excitatory and inhibitory features. Receptive-field features capture temporal and spectral modulations of natural stimuli. Ethological relevance of the stimulus affects the estimation of receptive-field dimensionality.

A two-stage spectral model for sound texture perception: Synthesis and psychophysics

The natural environment is filled with a variety of auditory events such as wind blowing, water flowing, and fire crackling. It has been suggested that the perception of such textural sounds is based on the statistics of the natural auditory events. Inspired by a recent spectral model for visual texture perception, we propose a model that can describe the perceived sound texture only with the linear spectrum and the energy spectrum. We tested the validity of the model by using synthetic noise sounds that preserve the two-stage amplitude spectra of the original sound. Psychophysical experiment showed that our synthetic noises were perceived as like the original sounds for 120 real-world auditory events. The performance was comparable with the synthetic sounds produced by McDermott-Simoncelli's model which considers various classes of auditory statistics. The results support the notion that the perception of natural sound textures is predictable by the two-stage spectral signals.

Characteristics of the Deconvolved Transient AEP from 80 Hz Steady-State Responses to Amplitude Modulation Stimulation

This study aimed to validate the existence and investigate the characteristics of the transient responses from conventional auditory steady-state responses (ASSRs) using deconvolution methods capable of dealing with amplitude modulated (AM) stimulation. Conventional ASSRs to seven stimulus rates were recorded from 17 participants. A deconvolution method was selected and modified to accommodate the AM stimulation. The calculated responses were examined in terms of temporal features with respect to different combinations of stimulus rates. Stable transient responses consisting of early stage brainstem responses and middle latency responses were reconstructed consistently for all rate combinations, which indicates that the superposition hypothesis is applicable to the generation of approximately 80 Hz ASSRs evoked by AM tones (AM-ASSRs). The new transient responses are characterized by three pairs of peak-troughs named as n₀p₀, n₁p₁, and n₂p₂ within 40 ms. Compared with conventional ABR-MLRs, the n₀p₀ indicates the first neural activity where p₀ might represent the main ABR components; the n₁ is the counterpart of N10; the p₂ is corresponding to the robust Pa at about 30 ms; the p₁ and n₂ are absent of real counterparts. The peak-peak amplitudes show a slight decrease with increasing stimulation rate from 75 to 95 Hz whereas the peak latencies change differently, which is consistent with the known rate-effect on AEPs. This is direct evidence for a transient response derived from AM-ASSRs for the first time. The characteristic components offer insight into the constitution of AM-ASSRs and may be promising in clinical applications and fundamental studies.

Mapping the human auditory cortex using spectrotemporal receptive fields generated with magnetoencephalography

We present a novel method to map the functional organization of the human auditory cortex noninvasively using magnetoencephalography (MEG). More specifically, this method estimates via reverse correlation the spectrotemporal receptive fields (STRF) in response to a temporally dense pure tone stimulus, from which important spectrotemporal characteristics of neuronal processing can be extracted and mapped back onto the cortex surface. We show that several neuronal populations can be found examining the spectrotemporal characteristics of their STRFs, and demonstrate how these can be used to generate tonotopic gradient maps. In doing so, we show that the spatial resolution of MEG is sufficient to reliably extract important information about the spatial organization of the auditory cortex, while enabling the analysis of complex temporal dynamics of auditory processing such as best temporal modulation rate and response latency given its excellent temporal resolution. Furthermore, because spectrotemporally dense auditory stimuli can be used with MEG, the time required to acquire the necessary data to generate tonotopic maps is significantly less for MEG than for other neuroimaging tools that acquire BOLD-like signals.

Cluster-based analysis improves predictive validity of spike-triggered receptive field estimates

Spectrotemporal receptive field (STRF) characterization is a central goal of auditory physiology. STRFs are often approximated by the spike-triggered average (STA), which reflects the average stimulus preceding a spike. In many cases, the raw STA is subjected to a threshold defined by gain values expected by chance. However, such correction methods have not been universally adopted, and the consequences of specific gain-thresholding approaches have not been investigated systematically. Here, we evaluate two classes of statistical correction techniques, using the resulting STRF estimates to predict responses to a novel validation stimulus. The first, more traditional technique eliminated STRF pixels (time-frequency bins) with gain values expected by chance. This correction method yielded significant increases in prediction accuracy, including when the threshold setting was optimized for each unit. The second technique was a two-step thresholding procedure wherein clusters of contiguous pixels surviving an initial gain threshold were then subjected to a cluster mass threshold based on summed pixel values. This approach significantly improved upon even the best gain-thresholding techniques. Additional analyses suggested that allowing threshold settings to vary independently for excitatory and inhibitory subfields of the STRF resulted in only marginal additional gains, at best. In summary, augmenting reverse correlation techniques with principled statistical correction choices increased prediction accuracy by over 80% for multi-unit STRFs and by over 40% for single-unit STRFs, furthering the interpretational relevance of the recovered spectrotemporal filters for auditory systems analysis.

Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns

Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2-4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice).

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.

Understanding the neurophysiological basis of auditory abilities for social communication: a perspective on the value of ethological paradigms

Acoustic communication between animals requires them to detect, discriminate, and categorize conspecific or heterospecific vocalizations in their natural environment. Laboratory studies of the auditory-processing abilities that facilitate these tasks have typically employed a broad range of acoustic stimuli, ranging from natural sounds like vocalizations to "artificial" sounds like pure tones and noise bursts. However, even when using vocalizations, laboratory studies often test abilities like categorization in relatively artificial contexts. Consequently, it is not clear whether neural and behavioral correlates of these tasks (1) reflect extensive operant training, which drives plastic changes in auditory pathways, or (2) the innate capacity of the animal and its auditory system. Here, we review a number of recent studies, which suggest that adopting more ethological paradigms utilizing natural communication contexts are scientifically important for elucidating how the auditory system normally processes and learns communication sounds. Additionally, since learning the meaning of communication sounds generally involves social interactions that engage neuromodulatory systems differently than laboratory-based conditioning paradigms, we argue that scientists need to pursue more ethological approaches to more fully inform our understanding of how the auditory system is engaged during acoustic communication. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives".

Functional localization of the auditory thalamus in individual human subjects

Here we describe an easily implemented protocol based on sparse MR acquisition and a scrambled 'music' auditory stimulus that allows for reliable measurement of functional activity within the medial geniculate body (MGB, the primary auditory thalamic nucleus) in individual subjects. We find that our method is equally accurate and reliable as previously developed structural methods, and offers significantly more accuracy in identifying the MGB than group based methods. We also find that lateralization and binaural summation within the MGB resemble those found in the auditory cortex.

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".

Learning of new sound categories shapes neural response patterns in human auditory cortex

The formation of new sound categories is fundamental to everyday goal-directed behavior. Categorization requires the abstraction of discrete classes from continuous physical features as required by context and task. Electrophysiology in animals has shown that learning to categorize novel sounds alters their spatiotemporal neural representation at the level of early auditory cortex. However, functional magnetic resonance imaging (fMRI) studies so far did not yield insight into the effects of category learning on sound representations in human auditory cortex. This may be due to the use of overlearned speech-like categories and fMRI subtraction paradigms, leading to insufficient sensitivity to distinguish the responses to learning-induced, novel sound categories. Here, we used fMRI pattern analysis to investigate changes in human auditory cortical response patterns induced by category learning. We created complex novel sound categories and analyzed distributed activation patterns during passive listening to a sound continuum before and after category learning. We show that only after training, sound categories could be successfully decoded from early auditory areas and that learning-induced pattern changes were specific to the category-distinctive sound feature (i.e., pitch). Notably, the similarity between fMRI response patterns for the sound continuum mirrored the sigmoid shape of the behavioral category identification function. Our results indicate that perceptual representations of novel sound categories emerge from neural changes at early levels of the human auditory processing hierarchy.

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.

The generation of direction selectivity in the auditory system

Both human speech and animal vocal signals contain frequency-modulated (FM) sounds. Although central auditory neurons that selectively respond to the direction of frequency modulation are known, the synaptic mechanisms underlying the generation of direction selectivity (DS) remain elusive. Here we show the emergence of DS neurons in the inferior colliculus by mapping the three major subcortical auditory nuclei. Cell-attached recordings reveal a highly reliable and precise firing of DS neurons to FM sweeps in a preferred direction. By using in vivo whole-cell current-clamp and voltage-clamp recordings, we found that the synaptic inputs to DS neurons are not direction selective, but temporally reversed excitatory and inhibitory synaptic inputs are evoked in response to opposing directions of FM sweeps. The construction of such temporal asymmetry, resulting DS, and its topography can be attributed to the spectral disparity of the excitatory and the inhibitory synaptic tonal receptive fields.

Dynamics of phase-independent spectro-temporal tuning in primary auditory cortex of the awake ferret

Tuning of cortical neurons is often measured as a static property, or during a steady-state regime, despite a number of studies suggesting that tuning depends on when it is measured during a neuron's response (e.g., onset vs. sustained vs. offset). We have previously shown that phase-locked tuning to feature transients evolves as a dynamic quantity from the onset of the sound. In this follow-up study, we examined the phase-independent tuning during feature transients. Based on previous results, we hypothesized phase-independent tuning should evolve on the same timescale as phase-locked tuning. We used stimuli of constant level, but alternating between flat spectro-temporal envelope and a modulated envelope with well-defined spectral density and temporal periodicity. This allowed the measure of changes in tuning to novel spectro-temporal content, as happens during running speech and other sounds with rapid transitions without a confounding change in sound level. For 95% of neurons, tuning changed significantly from the onset, over the course of the response. For a majority of these cells, the change occurred within the first 40ms following a feature onset, often even around 10-20ms. This solidifies the idea that tuning can change rapidly from onset tuning to the sustained, steady-state tuning.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

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.

Grip forces during object manipulation: experiment, mathematical model, and validation

When people transport handheld objects, they change the grip force with the object movement. Circular movement patterns were tested within three planes at two different rates (1.0, 1.5 Hz) and two diameters (20, 40 cm). Subjects performed the task reasonably well, matching frequencies and dynamic ranges of accelerations within expectations. A mathematical model was designed to predict the applied normal forces from kinematic data. The model is based on two hypotheses: (a) the grip force changes during movements along complex trajectories can be represented as the sum of effects of two basic commands associated with the parallel and orthogonal manipulation, respectively; (b) different central commands are sent to the thumb and virtual finger (Vf-four fingers combined). The model predicted the actual normal forces with a total variance accounted for of better than 98%. The effects of the two components of acceleration-along the normal axis and the resultant acceleration within the shear plane-on the digit normal forces are additive.

From elementary synaptic circuits to information processing in primary auditory cortex

A key for understanding how information is processed in the cortex is to unravel the dauntingly complex cortical neural circuitry. Recent technical innovations, in particular the in vivo whole-cell voltage-clamp recording techniques, make it possible to directly dissect the excitatory and inhibitory inputs underlying an individual cortical neuron's processing function. This method provides an essential complement to conventional approaches, with which the transfer functions of the neural system are derived by correlating neuronal spike outputs to sensory inputs. Here, we intend to introduce a potentially systematic strategy for resolving the structure of functional synaptic circuits. As complex circuits can be built upon elementary modules, the primary focus of this strategy is to identify elementary synaptic circuits and determine how these circuit units contribute to specific processing functions. This review will summarize recent studies on functional synaptic circuits in the primary auditory cortex, comment on existing experimental techniques for in vivo circuitry studies, and provide a perspective on immediate future directions.

Temporal coherence and attention in auditory scene analysis

Humans and other animals can attend to one of multiple sounds and follow it selectively over time. The neural underpinnings of this perceptual feat remain mysterious. Some studies have concluded that sounds are heard as separate streams when they activate well-separated populations of central auditory neurons, and that this process is largely pre-attentive. Here, we argue instead that stream formation depends primarily on temporal coherence between responses that encode various features of a sound source. Furthermore, we postulate that only when attention is directed towards a particular feature (e.g. pitch) do all other temporally coherent features of that source (e.g. timbre and location) become bound together as a stream that is segregated from the incoherent features of other sources.

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.

Competing streams at the cocktail party: exploring the mechanisms of attention and temporal integration

Processing of complex acoustic scenes depends critically on the temporal integration of sensory information as sounds evolve naturally over time. It has been previously speculated that this process is guided by both innate mechanisms of temporal processing in the auditory system, as well as top-down mechanisms of attention and possibly other schema-based processes. In an effort to unravel the neural underpinnings of these processes and their role in scene analysis, we combine magnetoencephalography (MEG) with behavioral measures in humans in the context of polyrhythmic tone sequences. While maintaining unchanged sensory input, we manipulate subjects' attention to one of two competing rhythmic streams in the same sequence. The results reveal that the neural representation of the attended rhythm is significantly enhanced in both its steady-state power and spatial phase coherence relative to its unattended state, closely correlating with its perceptual detectability for each listener. Interestingly, the data reveal a differential efficiency of rhythmic rates of the order of few hertz during the streaming process, closely following known neural and behavioral measures of temporal modulation sensitivity in the auditory system. These findings establish a direct link between known temporal modulation tuning in the auditory system (particularly at the level of auditory cortex) and the temporal integration of perceptual features in a complex acoustic scene, while mediated by processes of attention.

Nonlinearity of coding in primary auditory cortex of the awake ferret

Neural computation in sensory systems is often modeled as a linear system. This first order approximation is computed by reverse correlating a stimulus with the spike train it evokes. The spectro-temporal receptive field (STRF) is a generalization of this procedure which characterizes processing in the auditory pathway in both frequency and time. While the STRF performs well in predicting the overall course of the response to a novel stimulus, it is unable to account for aspects of the neural output which are inherently nonlinear (e.g. discrete events and non-negative spike rates). We measured the STRFs of neurons in the primary auditory cortex (AI) of the awake ferret using spectro-temporally modulated auditory gratings, or ripples. We quantified the degree of nonlinearity of these neurons by comparing their responses to the responses predicted from their respective STRFs. The responses of most cells in AI exhibited a squaring, nonlinear relation to the stimuli used to evoke them. Thus, the nonlinearity of these cells was nontrivial, that is it was not solely the result of spike rate rectification or saturation. By modeling the nonlinearity as a polynomial static output function, the predictive power of the STRF was significantly improved.

Multi-muscle synergies in a dual postural task: evidence for the principle of superposition

We used the framework of the uncontrolled manifold hypothesis to quantify multi-muscle synergies stabilizing the moment of force about the frontal axis (M(Y)) and the shear force in the anterior-posterior direction (F(X)) during voluntary body sway performed by standing subjects. We tested a hypothesis whether the controller could stabilize both M(Y) and F(X) at the same time when the task and the visual feedback was provided only on one of the variables (M(Y)). Healthy young subjects performed voluntary body sway in the anterior-posterior direction while different loads were attached at the ankle level producing horizontal forces acting forward or backwards. Principal component analysis was used to identify three M-modes within the space of integrated indices of muscle activation. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect a selected performance variable (M(Y) or F(X)) and the other that did. Under all loading conditions and for each performance variable, a higher value for the former variance component was found. We interpret these results as reflections of two multi-M-mode synergies stabilizing both F(X) and M(Y). The indices of synergies were modulated within the sway cycle; both performance variables were better stabilized when the body moved forward than when it moved backward. The results show that the controller can use a set of three elemental variables (M-modes) to stabilize two performance variables at the same time. No negative interference was seen between the synergy indices computed for the two performance variables supporting the principle of superposition with respect to multi-muscle postural control.

Long-lasting context dependence constrains neural encoding models in rodent auditory cortex

Acoustic processing requires integration over time. We have used in vivo intracellular recording to measure neuronal integration times in anesthetized rats. Using natural sounds and other stimuli, we found that synaptic inputs to auditory cortical neurons showed a rather long context dependence, up to > or =4 s (tau approximately 1 s), even though sound-evoked excitatory and inhibitory conductances per se rarely lasted greater, similar 100 ms. Thalamic neurons showed only a much faster form of adaptation with a decay constant tau <100 ms, indicating that the long-lasting form originated from presynaptic mechanisms in the cortex, such as synaptic depression. Restricting knowledge of the stimulus history to only a few hundred milliseconds reduced the predictable response component to about half that of the optimal infinite-history model. Our results demonstrate the importance of long-range temporal effects in auditory cortex and suggest a potential neural substrate for auditory processing that requires integration over timescales of seconds or longer, such as stream segregation.

Experience-dependent enhancement of linguistic pitch representation in the brainstem is not specific to a speech context

Neural representation of pitch is influenced by lifelong experiences with music and language at both cortical and subcortical levels of processing. The aim of this article is to determine whether neural plasticity for pitch representation at the level of the brainstem is dependent upon specific dimensions of pitch contours that commonly occur as part of a native listener's language experience. Brainstem frequency following responses (FFRs) were recorded from Chinese and English participants in response to four Mandarin tonal contours presented in a nonspeech context in the form of iterated rippled noise. Pitch strength (whole contour, 250 msec; 40-msec segments) and pitch-tracking accuracy (whole contour) were extracted from the FFRs using autocorrelation algorithms. Narrow band spectrograms were used to extract spectral information. Results showed that the Chinese group exhibits smoother pitch tracking than the English group in three out of the four tones. Moreover, cross-language comparisons of pitch strength of 40-msec segments revealed that the Chinese group exhibits more robust pitch representation of those segments containing rapidly changing pitch movements across all four tones. FFR spectral data were complementary showing that the Chinese group exhibits stronger representation of multiple pitch-relevant harmonics relative to the English group across all four tones. These findings support the view that at early preattentive stages of subcortical processing, neural mechanisms underlying pitch representation are shaped by particular dimensions of the auditory stream rather than speech per se. Adopting a temporal correlation analysis scheme for pitch encoding, we propose that long-term experience sharpens the tuning characteristics of neurons along the pitch axis with enhanced sensitivity to linguistically relevant variations in pitch.

Spectro-temporal modulation transfer function of single voxels in the human auditory cortex measured with high-resolution fMRI

Are visual and auditory stimuli processed by similar mechanisms in the human cerebral cortex? Images can be thought of as light energy modulations over two spatial dimensions, and low-level visual areas analyze images by decomposition into spatial frequencies. Similarly, sounds are energy modulations over time and frequency, and they can be identified and discriminated by the content of such modulations. An obvious question is therefore whether human auditory areas, in direct analogy to visual areas, represent the spectro-temporal modulation content of acoustic stimuli. To answer this question, we measured spectro-temporal modulation transfer functions of single voxels in the human auditory cortex with functional magnetic resonance imaging. We presented dynamic ripples, complex broadband stimuli with a drifting sinusoidal spectral envelope. Dynamic ripples are the auditory equivalent of the gratings often used in studies of the visual system. We demonstrate selective tuning to combined spectro-temporal modulations in the primary and secondary auditory cortex. We describe several types of modulation transfer functions, extracting different spectro-temporal features, with a high degree of interaction between spectral and temporal parameters. The overall low-pass modulation rate preference of the cortex matches the modulation content of natural sounds. These results demonstrate that combined spectro-temporal modulations are represented in the human auditory cortex, and suggest that complex signals are decomposed and processed according to their modulation content, the same transformation used by the visual system.

Dynamics of spectro-temporal tuning in primary auditory cortex of the awake ferret

We previously characterized the steady-state spectro-temporal tuning properties of cortical cells with respect to broadband sounds by using sounds with sinusoidal spectro-temporal modulation envelope where spectral density and temporal periodicity were constant over several seconds. However, since speech and other natural sounds have spectro-temporal features that change substantially over milliseconds, we study the dynamics of tuning by using stimuli of constant overall intensity, but alternating between a flat spectro-temporal envelope and a modulated envelope with well defined spectral density and temporal periodicity. This allows us to define the tuning of cortical cells to speech-like and other rapid transitions, on the order of milliseconds, as well as the time evolution of this tuning in response to the appearance of new features in a sound. Responses of 92 cells in AI were analyzed based on the temporal evolution of the following measures of tuning after a rapid transition in the stimulus: center of mass and breadth of tuning; separability and direction selectivity; temporal and spectral asymmetry. We find that tuning center of mass increased in 70% of cells for spectral density and in 68% of cells for temporal periodicity, while roughly half of cells (47%) broadened their tuning, with the other half (53%) sharpening tuning. The majority of cells (73%) were initially not direction selective, as measured by an inseparability index, which had an initial low value that then increased to a higher steady state value. Most cells were characterized by temporal symmetry, while spectral symmetry was initially high and then progressed to low steady-state values (61%). We demonstrate that cortical neurons can be characterized by a lag-dependent modulation transfer function. This characterization, when measured through to steady-state, becomes equivalent to the classical spectro-temporal receptive field.

Discrimination of auditory gratings in birds

Auditory gratings (also called auditory ripples) are a family of complex, broadband sounds with sinusoidally modulated logarithmic amplitudes and a drifting spectral envelope. These stimuli have been studied both physiologically in mammals and psychophysically in humans. Auditory gratings share spectro-temporal properties with many natural sounds, including species-specific vocalizations and the formant transitions of human speech. We successfully trained zebra finches and budgerigars, using operant conditioning methods, to discriminate between flat-spectrum broadband noise and noises with ripple spectra of different densities that moved up or down in frequency at various rates. Results show that discrimination thresholds (minimum modulation depth) increased as a function of increasing grating periodicity and density across all species. Results also show that discrimination in the two species of birds was better at those grating periodicities and densities that are prominent in their species-specific vocalizations. Budgerigars were generally more sensitive than both zebra finches and humans. Both bird species showed greater sensitivity to descending auditory gratings, which mirrors the main direction in their vocalizations. Humans, on the other hand, showed no directional preference even though speech is somewhat downward directional. Overall, our results are suggestive of both common strategies in the processing of complex sounds between birds and mammals and specialized, species-specific variations on that processing in birds.

Wiener-Volterra characterization of neurons in primary auditory cortex using poisson-distributed impulse train inputs

An extension of the Wiener-Volterra theory to a Poisson-distributed impulse train input was used to characterize the temporal response properties of neurons in primary auditory cortex (AI) of the ketamine-anesthetized cat. Both first- and second-order "Poisson-Wiener" (PW) models were tested on their predictions of temporal modulation transfer functions (tMTFs), which were derived from extracellular spike responses to periodic click trains with click repetition rates of 2-64 Hz. Second-order (i.e., nonlinear) PW fits to the measured tMTFs could be described as very good in a majority of cases (e.g., predictability >or=80%) and were almost always superior to first-order (i.e., linear) fits. In all sampled neurons, second-order PW kernels showed strong compressive nonlinearities (i.e., a depression of the impulse response) but never expansive nonlinearities (i.e., a facilitation of the impulse response). In neurons with low-pass tMTFs, the depression decayed exponentially with the interstimulus lag, whereas in neurons with band-pass tMTFs, the depression was typically double-peaked, and the second peak occurred at a lag that correlated with the neuron's best modulation frequency. It appears that modulation-tuning in AI arises in part from an interplay of two nonlinear processes with distinct time courses.

Temporal coherence in the perceptual organization and cortical representation of auditory scenes

Just as the visual system parses complex scenes into identifiable objects, the auditory system must organize sound elements scattered in frequency and time into coherent "streams." Current neurocomputational theories of auditory streaming rely on tonotopic organization of the auditory system to explain the observation that sequential spectrally distant sound elements tend to form separate perceptual streams. Here, we show that spectral components that are well separated in frequency are no longer heard as separate streams if presented synchronously rather than consecutively. In contrast, responses from neurons in primary auditory cortex of ferrets show that both synchronous and asynchronous tone sequences produce comparably segregated responses along the tonotopic axis. The results argue against tonotopic separation per se as a neural correlate of stream segregation. Instead we propose a computational model of stream segregation that can account for the data by using temporal coherence as the primary criterion for predicting stream formation.

Organizing principles of spectro-temporal encoding in the avian primary auditory area field L

The organization of postthalamic auditory areas remains unclear in many respects. Using a stimulus based on properties of natural sounds, we mapped spectro-temporal receptive fields (STRFs) of neurons in the primary auditory area field L of unanesthetized zebra finches. Cells were sensitive to only a subset of possible acoustic features: nearly all neurons were narrowly tuned along the spectral dimension, the temporal dimension, or both; broadly tuned and strongly orientation-sensitive cells were rare. At high stimulus intensities, neurons were sensitive to differences in sound energy along their preferred dimension, while at lower intensities, neurons behaved more like simple detectors. Finally, we found a systematic relationship between neurons' STRFs, their electrophysiological properties, and their location in field L input or output layers. These data suggest that spectral and temporal processing are segregated within field L, and provide a unifying account of how field L response properties depend on stimulus intensity.

The linearity of emergent spectro-temporal receptive fields in a model of auditory cortex

The responses of cortical neurons are often characterized by measuring their spectro-temporal receptive fields (STRFs). The STRF of a cell can be thought of as a representation of its stimulus 'preference' but it is also a filter or 'kernel' that represents the best linear prediction of the response of that cell to any stimulus. A range of in vivo STRFs with varying properties have been reported in various species, although none in humans. Using a computational model it has been shown that responses of ensembles of artificial STRFs, derived from limited sets of formative stimuli, preserve information about utterance class and prosody as well as the identity and sex of the speaker in a model speech classification system. In this work we help to put this idea on a biologically plausible footing by developing a simple model thalamo-cortical system built of conductance based neurons and synapses some of which exhibit spike-time-dependent plasticity. We show that the neurons in such a model when exposed to formative stimuli develop STRFs with varying temporal properties exhibiting a range of heterotopic integration. These model neurons also, in common with neurons measured in vivo, exhibit a wide range of non-linearities; this deviation from linearity can be exposed by characterizing the difference between the measured response of each neuron to a stimulus, and the response predicted by the STRF estimated for that neuron. The proposed model, with its simple architecture, learning rule, and modest number of neurons (<1000), is suitable for implementation in neuromorphic analogue VLSI hardware and hence could form the basis of a developmental, real time, neuromorphic sound classification system.

Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods

The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus-response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.

Applications of static and dynamic iterated rippled noise to evaluate pitch encoding in the human auditory brainstem

This paper presents a new application of the dynamic iterated rippled noise (IRN) algorithm by generating dynamic pitch contours representative of those that occur in natural speech in the context of EEG and the frequency following response (FFR). Besides IRN steady state and linear rising stimuli, curvilinear rising stimuli were modeled after pitch contours of natural productions of Mandarin Tone 2. Electrophysiological data on pitch representation at the level of the brainstem, as reflected in FFR, were evaluated for all stimuli, static or dynamic. Autocorrelation peaks were observed corresponding to the fundamental period (tau) as well as spectral bands at the fundamental and its harmonics for both a low and a high iteration step. At the higher iteration step, both spectral and temporal FFR representations were more robust, indicating that both acoustic properties may be utilized for pitch extraction at the level of the brainstem. By applying curvilinear IRN stimuli to elicit FFRs, we can evaluate the effects of temporal degradation on 1) the neural representation of linguistically-relevant pitch features in a target population (e.g., cochlear implant) and 2) the efficacy of signal processing schemes in conventional hearing aids and cochlear implants to recover these features.

The consequences of response nonlinearities for interpretation of spectrotemporal receptive fields

Neurons in the central auditory system are often described by the spectrotemporal receptive field (STRF), conventionally defined as the best linear fit between the spectrogram of a sound and the spike rate it evokes. An STRF is often assumed to provide an estimate of the receptive field of a neuron, i.e., the spectral and temporal range of stimuli that affect the response. However, when the true stimulus-response function is nonlinear, the STRF will be stimulus dependent, and changes in the stimulus properties can alter estimates of the sign and spectrotemporal extent of receptive field components. We demonstrate analytically and in simulations that, even when uncorrelated stimuli are used, interactions between simple neuronal nonlinearities and higher-order structure in the stimulus can produce STRFs that show contributions from time-frequency combinations to which the neuron is actually insensitive. Only when spectrotemporally independent stimuli are used does the STRF reliably indicate features of the underlying receptive field, and even then it provides only a conservative estimate. One consequence of these observations, illustrated using natural stimuli, is that a stimulus-induced change in an STRF could arise from a consistent but nonlinear neuronal response to stimulus ensembles with differing higher-order dependencies. Thus, although the responses of higher auditory neurons may well involve adaptation to the statistics of different stimulus ensembles, stimulus dependence of STRFs alone, or indeed of any overly constrained stimulus-response mapping, cannot demonstrate the nature or magnitude of such effects.

Phoneme representation and classification in primary auditory cortex

A controversial issue in neurolinguistics is whether basic neural auditory representations found in many animals can account for human perception of speech. This question was addressed by examining how a population of neurons in the primary auditory cortex (A1) of the naive awake ferret encodes phonemes and whether this representation could account for the human ability to discriminate them. When neural responses were characterized and ordered by spectral tuning and dynamics, perceptually significant features including formant patterns in vowels and place and manner of articulation in consonants, were readily visualized by activity in distinct neural subpopulations. Furthermore, these responses faithfully encoded the similarity between the acoustic features of these phonemes. A simple classifier trained on the neural representation was able to simulate human phoneme confusion when tested with novel exemplars. These results suggest that A1 responses are sufficiently rich to encode and discriminate phoneme classes and that humans and animals may build upon the same general acoustic representations to learn boundaries for categorical and robust sound classification.

Stability of spectro-temporal tuning over several seconds in primary auditory cortex of the awake ferret

The steady-state spectro-temporal tuning of auditory cortical cells has been studied using a variety of broadband stimuli that characterize neurons by their steady-state responses to long duration stimuli, lasting from about a second to several minutes. Central sensory stations are thought to adapt in their response to stimuli presented over extended periods of time. For instance, we have previously shown that auditory cortical neurons display a second order of adaptation, whereby the rate of their adaptation to the repeated presentation of fixed alternating stimuli decreases with each presentation. The auditory grating (or ripple) method of characterizing central auditory neurons, and its extensions, have proven very effective. But these stimuli are typically used with spectro-temporal content held fixed over time-scales of seconds, introducing the possibility of rapid adaptation while the receptive field is being measured, whereas the neural response used to compute a spectro-temporal receptive field (STRF) assumes stationarity in the neural input/output function. We demonstrate dynamic changes in some parameters during the measurement of the STRF over a period of seconds, even absent of a relevant behavioral task. Specifically, we find in the primary auditory cortex of the awake ferret, small but systematic changes in duration and breadth of tuning of STRFs when comparing the early (0.25-1.75 s) and late (4.5-6 s) segments of the responses to these stimuli.

Spectral modulation masking patterns reveal tuning to spectral envelope frequency

Auditory processing appears to include a series of domain-specific filtering operations that include tuning in the audio-frequency domain, followed by tuning in the temporal modulation domain, and perhaps tuning in the spectral modulation domain. To explore the possibility of tuning in the spectral modulation domain, a masking experiment was designed to measure masking patterns in the spectral modulation domain. Spectral modulation transfer functions (SMTFs) were measured for modulation frequencies from 0.25 to 14 cycles/octave superimposed on noise carriers either one octave (800-1600 Hz, 6400-12,800 Hz) or six octaves wide (200-12,800 Hz). The resulting SMTFs showed maximum sensitivity to modulation between 1 and 3 cycles/octave with reduced sensitivity above and below this region. Masked spectral modulation detection thresholds were measured for masker modulation frequencies of 1, 3, and 5 cycles/octave with a fixed modulation depth of 15 dB. The masking patterns obtained for each masker frequency and carrier band revealed tuning (maximum masking) near the masker frequency, which is consistent with the theory that spectral envelope perception is governed by a series of spectral modulation channels tuned to different spectral modulation frequencies.

Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2min of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.

Tuning to natural stimulus dynamics in primary auditory cortex

The amplitude and pitch fluctuations of natural soundscapes often exhibit "1/f spectra", which means that large, abrupt changes in pitch or loudness occur proportionally less frequently in nature than gentle, gradual fluctuations. Furthermore, human listeners reportedly prefer 1/f distributed random melodies to melodies with faster (1/f0) or slower (1/f2) dynamics. One might therefore suspect that neurons in the central auditory system may be tuned to 1/f dynamics, particularly given that recent reports provide evidence for tuning to 1/f dynamics in primary visual cortex. To test whether neurons in primary auditory cortex (A1) are tuned to 1/f dynamics, we recorded responses to random tone complexes in which the fundamental frequency and the envelope were determined by statistically independent "1/f(gamma) random walks," with gamma set to values between 0.5 and 4. Many A1 neurons showed clear evidence of tuning and responded with higher firing rates to stimuli with gamma between 1 and 1.5. Response patterns elicited by 1/f(gamma) stimuli were more reproducible for values of gamma close to 1. These findings indicate that auditory cortex is indeed tuned to the 1/f dynamics commonly found in the statistical distributions of natural soundscapes.

Temporal processing and adaptation in the songbird auditory forebrain

Songbird auditory neurons must encode the dynamics of natural sounds at many volumes. We investigated how neural coding depends on the distribution of stimulus intensities. Using reverse-correlation, we modeled responses to amplitude-modulated sounds as the output of a linear filter and a nonlinear gain function, then asked how filters and nonlinearities depend on the stimulus mean and variance. Filter shape depended strongly on mean amplitude (volume): at low mean, most neurons integrated sound over many milliseconds, while at high mean, neurons responded more to local changes in amplitude. Increasing the variance (contrast) of amplitude modulations had less effect on filter shape but decreased the gain of firing in most cells. Both filter and gain changes occurred rapidly after a change in statistics, suggesting that they represent nonlinearities in processing. These changes may permit neurons to signal effectively over a wider dynamic range and are reminiscent of findings in other sensory systems.

Masking by inaudible sounds and the linearity of temporal summation

Many natural sounds, including speech and animal vocalizations, involve rapid sequences that vary in spectrum and amplitude. Each sound within a sequence has the potential to affect the audibility of subsequent sounds in a process known as forward masking. Little is known about the neural mechanisms underlying forward masking, particularly in more realistic situations in which multiple sounds follow each other in rapid succession. A parsimonious hypothesis is that the effects of consecutive sounds combine linearly, so that the total masking effect is a simple sum of the contributions from the individual maskers. The experiment reported here tests a counterintuitive prediction of this linear-summation hypothesis, namely that a sound that itself is inaudible should, under certain circumstances, affect the audibility of subsequent sounds. The results show that, when two forward maskers are combined, the second of the two maskers can continue to produce substantial masking, even when it is completely masked by the first masker. Thus, inaudible sounds can affect the perception of subsequent sounds. A model incorporating instantaneous compression (reflecting the nonlinear response of the basilar membrane in the cochlea), followed by linear summation of the effects of the maskers, provides a good account of the data. Despite the presence of multiple sources of nonlinearity in the auditory system, masking effects by sequential sounds combine in a manner that is well captured by a time-invariant linear system.

Response adaptation to broadband sounds in primary auditory cortex of the awake ferret

Driven by previous reports of adaptation to persistent stimuli in other brain regions, we investigated adaptive effects in the Primary Auditory Cortex of awake non-behaving ferrets (Mustela putorius furo). Electrophysiological data was obtained in response to the presentation of auditory gratings with a structured spectro-temporal envelope of varying bandwidth which had repeated transitions between low and high modulation depths. The responses were analyzed in terms of the evoked spike rates and in terms of the degree of phase locking to the modulation. We found two populations of cells, both of which showed adaptation in the traditional sense. For one population, we also found a second order of adaptation--i.e., adaptation of the adaptation. This suggests the existence of at least two coding strategies which differ in the weight placed on sensory context.

Response linearity in primary auditory cortex of the ferret

The responses of neurons within the primary auditory cortex (A1) of the ferret elicited by broadband dynamic spectral ripple stimuli were examined over a range of ripple spectral densities and ripple velocities. The large majority of neurons showed modulated responses to these stimuli and responded most strongly at low ripple densities and velocities. The period histograms of their responses were subjected to Fourier analysis, and the ratio of the magnitudes of the f1 and fo (DC) components of these responses were calculated to give a quantitative index of response linearity. For 82 out of 396 neurons tested (20.7%) this ratio remained above 1.0 over the entire range of ripple densities and velocities. These neurons were classified as 'consistently linear'. A further 134/396 (33.8%) of neurons maintained an f1/f0 ratio above 1.0 for either a range of ripple densities at a fixed ripple velocity, or over a range of ripple velocities at a specific ripple density, and were classified as 'locally linear'. Interestingly, for the superficial layers of the primary auditory cortex, consistently linear and locally linear neurons outnumbered nonlinear neurons by a 2:1 ratio. The converse was true for the deep layers. Unlike in primary visual cortex, where f1/f0 ratios have been reported to exhibit a bimodal distribution with a minimum at f1/f0 = 1, f1/f0 ratios for A1 are unimodally distributed with a peak at f1/f0 = 1.

Sparse representations for the cocktail party problem

A striking feature of many sensory processing problems is that there appear to be many more neurons engaged in the internal representations of the signal than in its transduction. For example, humans have approximately 30,000 cochlear neurons, but at least 1000 times as many neurons in the auditory cortex. Such apparently redundant internal representations have sometimes been proposed as necessary to overcome neuronal noise. We instead posit that they directly subserve computations of interest. Here we provide an example of how sparse overcomplete linear representations can directly solve difficult acoustic signal processing problems, using as an example monaural source separation using solely the cues provided by the differential filtering imposed on a source by its path from its origin to the cochlea [the head-related transfer function (HRTF)]. In contrast to much previous work, the HRTF is used here to separate auditory streams rather than to localize them in space. The experimentally testable predictions that arise from this model, including a novel method for estimating the optimal stimulus of a neuron using data from a multineuron recording experiment, are generic and apply to a wide range of sensory computations.

Circuitry and the classification of simple and complex cells in V1

Based on a large-scale neural network model of striate cortex (V1), we present a simulation study of extra- and intracellular response modulations for drifting and contrast reversal grating stimuli. Specifically, we study the dependency of these modulations on the neural circuitry. We find that the frequently used ratio of the first harmonic to the mean response to classify simple and complex cells is highly insensitive to circuitry. Limited experimental sample size for the distribution of this measure makes it unsuitable for distinguishing whether the dichotomy of simple and complex cells originates from distinct LGN axon connectivity and/or local circuitry in V1. We show that a possible useful measure in this respect is the ratio of the intracellular second- to first-harmonic response for contrast reversal gratings. This measure is highly sensitive to neural circuitry and its distribution can be sampled with sufficient accuracy from a limited amount of experimental data. Further, the distribution of this measure is qualitatively similar to that of the subfield correlation coefficient, although it is more robust and easier to obtain experimentally.