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Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds

Overview of attention for article published in PLoS Computational Biology, March 2013
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Title
Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds
Published in
PLoS Computational Biology, March 2013
DOI 10.1371/journal.pcbi.1002982
Pubmed ID
Authors

Michael A. Carlin, Mounya Elhilali

Abstract

The processing characteristics of neurons in the central auditory system are directly shaped by and reflect the statistics of natural acoustic environments, but the principles that govern the relationship between natural sound ensembles and observed responses in neurophysiological studies remain unclear. In particular, accumulating evidence suggests the presence of a code based on sustained neural firing rates, where central auditory neurons exhibit strong, persistent responses to their preferred stimuli. Such a strategy can indicate the presence of ongoing sounds, is involved in parsing complex auditory scenes, and may play a role in matching neural dynamics to varying time scales in acoustic signals. In this paper, we describe a computational framework for exploring the influence of a code based on sustained firing rates on the shape of the spectro-temporal receptive field (STRF), a linear kernel that maps a spectro-temporal acoustic stimulus to the instantaneous firing rate of a central auditory neuron. We demonstrate the emergence of richly structured STRFs that capture the structure of natural sounds over a wide range of timescales, and show how the emergent ensembles resemble those commonly reported in physiological studies. Furthermore, we compare ensembles that optimize a sustained firing code with one that optimizes a sparse code, another widely considered coding strategy, and suggest how the resulting population responses are not mutually exclusive. Finally, we demonstrate how the emergent ensembles contour the high-energy spectro-temporal modulations of natural sounds, forming a discriminative representation that captures the full range of modulation statistics that characterize natural sound ensembles. These findings have direct implications for our understanding of how sensory systems encode the informative components of natural stimuli and potentially facilitate multi-sensory integration.

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Geographical breakdown

Country Count As %
United States 11 16%
United Kingdom 1 1%
Germany 1 1%
France 1 1%
Unknown 56 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 33%
Researcher 18 26%
Student > Master 6 9%
Student > Bachelor 4 6%
Professor > Associate Professor 4 6%
Other 8 11%
Unknown 7 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 23%
Neuroscience 11 16%
Engineering 9 13%
Psychology 5 7%
Computer Science 4 6%
Other 12 17%
Unknown 13 19%