Hidden neural states underlie canary song syntax

Yarden Cohen (), Jun Shen, Dawit Semu, Daniel P. Leman, William A. Liberti, L. Nathan Perkins, Derek C. Liberti, Darrell N. Kotton and Timothy J. Gardner ()
Additional contact information
Yarden Cohen: Boston University
Jun Shen: Boston University Center for Systems Neuroscience
Dawit Semu: Boston University
Daniel P. Leman: Boston University
William A. Liberti: Boston University
L. Nathan Perkins: Boston University
Derek C. Liberti: Center for Regenerative Medicine of Boston University and Boston Medical Center
Darrell N. Kotton: Center for Regenerative Medicine of Boston University and Boston Medical Center
Timothy J. Gardner: Boston University

Nature, 2020, vol. 582, issue 7813, 539-544

Abstract: Abstract Coordinated skills such as speech or dance involve sequences of actions that follow syntactic rules in which transitions between elements depend on the identities and order of past actions. Canary songs consist of repeated syllables called phrases, and the ordering of these phrases follows long-range rules1 in which the choice of what to sing depends on the song structure many seconds prior. The neural substrates that support these long-range correlations are unknown. Here, using miniature head-mounted microscopes and cell-type-specific genetic tools, we observed neural activity in the premotor nucleus HVC2–4 as canaries explored various phrase sequences in their repertoire. We identified neurons that encode past transitions, extending over four phrases and spanning up to four seconds and forty syllables. These neurons preferentially encode past actions rather than future actions, can reflect more than one song history, and are active mostly during the rare phrases that involve history-dependent transitions in song. These findings demonstrate that the dynamics of HVC include ‘hidden states’ that are not reflected in ongoing behaviour but rather carry information about prior actions. These states provide a possible substrate for the control of syntax transitions governed by long-range rules.

Date: 2020
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DOI: 10.1038/s41586-020-2397-3

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