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

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TLDR
Using simulations, it is 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, which suggests that synaptic depression may enable efficient encoding of natural auditory stimuli.
Abstract
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.

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Citations
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Journal ArticleDOI

Normalization as a canonical neural computation.

TL;DR: Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions, suggesting that it serves as a canonical neural computation.
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Neural coding of continuous speech in auditory cortex during monaural and dichotic listening

TL;DR: These findings characterize how the spectrotemporal features of speech are encoded in human auditory cortex and establish a single-trial-based paradigm to study the neural basis underlying the cocktail party phenomenon.
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A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy

TL;DR: A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds, and hierarchical neural networks for speech and music recognition were optimized to solve ecologically relevant tasks.
Journal ArticleDOI

Contrast gain control in auditory cortex.

TL;DR: It is shown that neurons in ferret auditory cortex rescale their gain to partially compensate for the spectrotemporal contrast of recent stimulation, which may be seeking an efficient coding of natural sounds.
Journal ArticleDOI

Adaptive, behaviorally gated, persistent encoding of task-relevant auditory information in ferret frontal cortex

TL;DR: Compared activity in ferret frontal cortex and primary auditory cortex during auditory and visual tasks requiring discrimination between classes of reference and target stimuli suggests that A1 and frontal cortex dynamically establish a functional connection during auditory behavior that shapes the flow of sensory information and maintains a persistent trace of recent task-relevant stimulus features.
References
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Additive Logistic Regression : A Statistical View of Boosting

TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.
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Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex

TL;DR: A normalization model is proposed, which extends the linear model of simple cells in the primary visual cortex to include mutual shunting inhibition among a large number of cortical cells, and its effect in the model is to normalize the linear responses by a measure of stimulus energy.
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Neural networks with dynamic synapses

TL;DR: A unified phenomenological model is proposed that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential activity in a presynaptic population and allows for derivation of mean-field equations, which govern the activity of large, interconnected networks.
Journal ArticleDOI

Efficient auditory coding

TL;DR: It is shown that, for natural sounds, the complete acoustic waveform can be represented efficiently with a nonlinear model based on a population spike code, which shows striking similarities to time-domain cochlear filter estimates, have a frequency-bandwidth dependence similar to that of auditory nerve fibres, and yield significantly greater coding efficiency than conventional signal representations.
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