Spectrotemporal contrast kernels for neurons in primary auditory cortex

Insult-induced adaptive plasticity of the auditory system

The brain displays a remarkable capacity for both widespread and region-specific modifications in response to environmental challenges, with adaptive processes bringing about the reweighing of connections in neural networks putatively required for optimizing performance and behavior. As an avenue for investigation, studies centered around changes in the mammalian auditory system, extending from the brainstem to the cortex, have revealed a plethora of mechanisms that operate in the context of sensory disruption after insult, be it lesion-, noise trauma, drug-, or age-related. Of particular interest in recent work are those aspects of auditory processing which, after sensory disruption, change at multiple—if not all—levels of the auditory hierarchy. These include changes in excitatory, inhibitory and neuromodulatory networks, consistent with theories of homeostatic plasticity; functional alterations in gene expression and in protein levels; as well as broader network processing effects with cognitive and behavioral implications. Nevertheless, there abounds substantial debate regarding which of these processes may only be sequelae of the original insult, and which may, in fact, be maladaptively compelling further degradation of the organism's competence to cope with its disrupted sensory context. In this review, we aim to examine how the mammalian auditory system responds in the wake of particular insults, and to disambiguate how the changes that develop might underlie a correlated class of phantom disorders, including tinnitus and hyperacusis, which putatively are brought about through maladaptive neuroplastic disruptions to auditory networks governing the spatial and temporal processing of acoustic sensory information.

Integration over multiple timescales in primary auditory cortex

Speech and other natural vocalizations are characterized by large modulations in their sound envelope. The timing of these modulations contains critical information for discrimination of important features, such as phonemes. We studied how depression of synaptic inputs, a mechanism frequently reported in cortex, can contribute to the encoding of envelope dynamics. Using a nonlinear stimulus-response model that accounted for synaptic depression, we predicted responses of neurons in ferret primary auditory cortex (A1) to stimuli with natural temporal modulations. The depression model consistently performed better than linear and second-order models previously used to characterize A1 neurons, and it produced more biologically plausible fits. To test how synaptic depression can contribute to temporal stimulus integration, we used nonparametric maximum a posteriori decoding to compare the ability of neurons showing and not showing depression to reconstruct the stimulus envelope. Neurons showing evidence for depression reconstructed stimuli over a longer range of latencies. These findings suggest that variation in depression across the cortical population supports a rich code for representing the temporal dynamics of natural sounds.

Constructing noise-invariant representations of sound in the auditory pathway

Along the auditory pathway from auditory nerve to midbrain to cortex, individual neurons adapt progressively to sound statistics, enabling the discernment of foreground sounds, such as speech, over background noise.

Identifying behaviorally relevant sounds in the presence of background noise is one of the most important and poorly understood challenges faced by the auditory system. An elegant solution to this problem would be for the auditory system to represent sounds in a noise-invariant fashion. Since a major effect of background noise is to alter the statistics of the sounds reaching the ear, noise-invariant representations could be promoted by neurons adapting to stimulus statistics. Here we investigated the extent of neuronal adaptation to the mean and contrast of auditory stimulation as one ascends the auditory pathway. We measured these forms of adaptation by presenting complex synthetic and natural sounds, recording neuronal responses in the inferior colliculus and primary fields of the auditory cortex of anaesthetized ferrets, and comparing these responses with a sophisticated model of the auditory nerve. We find that the strength of both forms of adaptation increases as one ascends the auditory pathway. To investigate whether this adaptation to stimulus statistics contributes to the construction of noise-invariant sound representations, we also presented complex, natural sounds embedded in stationary noise, and used a decoding approach to assess the noise tolerance of the neuronal population code. We find that the code for complex sounds in the periphery is affected more by the addition of noise than the cortical code. We also find that noise tolerance is correlated with adaptation to stimulus statistics, so that populations that show the strongest adaptation to stimulus statistics are also the most noise-tolerant. This suggests that the increase in adaptation to sound statistics from auditory nerve to midbrain to cortex is an important stage in the construction of noise-invariant sound representations in the higher auditory brain.

We rarely hear sounds (such as someone talking) in isolation, but rather against a background of noise. When mixtures of sounds and background noise reach the ears, peripheral auditory neurons represent the whole sound mixture. Previous evidence suggests, however, that the higher auditory brain represents just the sounds of interest, and is less affected by the presence of background noise. The neural mechanisms underlying this transformation are poorly understood. Here, we investigate these mechanisms by studying the representation of sound by populations of neurons at three stages along the auditory pathway; we simulate the auditory nerve and record from neurons in the midbrain and primary auditory cortex of anesthetized ferrets. We find that the transformation from noise-sensitive representations of sound to noise-tolerant processing takes place gradually along the pathway from auditory nerve to midbrain to cortex. Our results suggest that this results from neurons adapting to the statistics of heard sounds.

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.

Adaptive stimulus optimization for sensory systems neuroscience

In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison.