Computational model of avian nervous system nuclei governing learned song

Eve Armstrong

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arXiv · 1801.06226

Computational model of avian nervous system nuclei governing learned song

Eve Armstrong

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Abstract

The means by which neuronal activity yields robust behavior is a ubiquitous question in neuroscience. In the songbird, the timing of a highly stereotyped song motif is attributed to the cortical nucleus HVC, and to feedback to HVC from downstream nuclei in the song motor pathway. Control of the acoustic structure appears to be shared by various structures, whose functional connectivity is largely unknown. Currently there exists no model for functional synaptic architecture that links HVC to song output in a manner consistent with experiments. Here we build on a previous model of HVC in which a distinct functional architecture may act as a pattern generator to drive downstream regions. Using a specific functional connectivity of the song motor pathway, we show how this HVC mechanism can generate simple representations of the driving forces for song. The model reproduces observed correlations between neuronal and respiratory activity and acoustic features of song. It makes testable predictions regarding the electrophysiology of distinct populations in the robust nucleus of the arcopallium (RA), the connectivity within HVC and RA and between them, and the activity patterns of vocal-respiratory neurons in the brainstem.

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Eve Armstrong. 2018-01-18. Computational model of avian nervous system nuclei governing learned song. https://arxiv.org/abs/1801.06226

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