Modeling attention-driven plasticity in auditory cortical receptive fields

Frontiers in Computational Neuroscience
Michael A Carlin, Mounya Elhilali

Abstract

To navigate complex acoustic environments, listeners adapt neural processes to focus on behaviorally relevant sounds in the acoustic foreground while minimizing the impact of distractors in the background, an ability referred to as top-down selective attention. Particularly striking examples of attention-driven plasticity have been reported in primary auditory cortex via dynamic reshaping of spectro-temporal receptive fields (STRFs). By enhancing the neural response to features of the foreground while suppressing those to the background, STRFs can act as adaptive contrast matched filters that directly contribute to an improved cognitive segregation between behaviorally relevant and irrelevant sounds. In this study, we propose a novel discriminative framework for modeling attention-driven plasticity of STRFs in primary auditory cortex. The model describes a general strategy for cortical plasticity via an optimization that maximizes discriminability between the foreground and distractors while maintaining a degree of stability in the cortical representation. The first instantiation of the model describes a form of feature-based attention and yields STRF adaptation patterns consistent with a contrast matched filter previously repo...Continue Reading

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Citations

Jan 4, 2017·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Emine Merve Kaya, Mounya Elhilali
Aug 28, 2021·PLoS Computational Biology·Kenny F Chou, Kamal Sen

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MATLAB
CVX

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