Local field potentials in a pre-motor region predict learned vocal sequences.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
09 2021
Historique:
received: 24 06 2020
accepted: 08 07 2021
entrez: 23 9 2021
pubmed: 24 9 2021
medline: 15 12 2021
Statut: epublish

Résumé

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity detail patterns of sequential bursting in small, carefully identified subsets of neurons in the HVC population. The dynamics of HVC are well described by these characterizations, but have not been verified beyond this scale of measurement. There is a rich history of using local field potentials (LFP) to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time, and they have been used to study and decode human speech and other complex motor behaviors. Here we characterize LFP signals presumptively from the HVC of freely behaving male zebra finches during song production to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time-varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates the utility of LFP for studying vocal behavior in songbirds. Surprisingly, the time frequency structure of HVC LFP is qualitatively similar to well-established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight into common computational principles for learning and/or generating complex motor-vocal behaviors.

Identifiants

pubmed: 34555020
doi: 10.1371/journal.pcbi.1008100
pii: PCOMPBIOL-D-20-01108
pmc: PMC8460039
doi:

Banques de données

figshare
['10.6084/m9.figshare.15094219', '10.6084/m9.figshare.14502198.v2']

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1008100

Subventions

Organisme : NIDCD NIH HHS
ID : R01 DC018446
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC008358
Pays : United States
Organisme : NIDCD NIH HHS
ID : R01 DC018055
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM134363
Pays : United States

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: V.G. holds shares in Neuralink, Corp., and Paradromics, Inc., and currently consults for Paradromics, Inc. These organizations had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Auteurs

Daril E Brown (DE)

Department of Electrical and Computer Engineering, University of California, San Diego, California, United States of America.
Department of Psychology, University of California, San Diego, California, United States of America.

Jairo I Chavez (JI)

Department of Cognitive Science, University of California, San Diego, California, United States of America.

Derek H Nguyen (DH)

Department of Bioengineering, University of California, San Diego, California, United States of America.

Adam Kadwory (A)

Department of Bioengineering, University of California, San Diego, California, United States of America.

Bradley Voytek (B)

Department of Cognitive Science, University of California, San Diego, California, United States of America.
Kavli Institute for Brain and Mind, University of California, San Diego, California, United States of America.
Halıcıoğlu Data Science Institute, University of California, San Diego, California, United States of America.
Neurosciences Graduate Program, University of California, San Diego, California, United States of America.

Ezequiel M Arneodo (EM)

Department of Psychology, University of California, San Diego, California, United States of America.
IFLP-CONICET, Departamento de Física, Universidad Nacional de La Plata, La Plata, Argentina.

Timothy Q Gentner (TQ)

Department of Psychology, University of California, San Diego, California, United States of America.
Kavli Institute for Brain and Mind, University of California, San Diego, California, United States of America.
Neurosciences Graduate Program, University of California, San Diego, California, United States of America.
Neurobiology Section, Division of Biological Sciences, University of California, San Diego, California, United States of America.

Vikash Gilja (V)

Department of Electrical and Computer Engineering, University of California, San Diego, California, United States of America.
Kavli Institute for Brain and Mind, University of California, San Diego, California, United States of America.
Neurosciences Graduate Program, University of California, San Diego, California, United States of America.

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