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Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

View ORCID ProfileDaril E. Brown II, View ORCID ProfileJairo I. Chavez, View ORCID ProfileDerek H. Nguyen, Adam Kadwory, Bradley Voytek, View ORCID ProfileEzequiel Arneodo, View ORCID ProfileTimothy Q. Gentner, Vikash Gilja
doi: https://doi.org/10.1101/2020.06.30.179861
Daril E. Brown II
1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
2Department of Psychology, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Jairo I. Chavez
1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Derek H. Nguyen
1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Adam Kadwory
1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Bradley Voytek
3Department of Cognitive Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
5Kavli Institute for Brain and Mind, 9500 Gilman Dr, La Jolla, CA, USA
6Halıcıoğlu Data Science Institute, 9500 Gilman Dr, La Jolla, CA, USA
7Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Ezequiel Arneodo
4Biocircuits Institute, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Timothy Q. Gentner
2Department of Psychology, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
5Kavli Institute for Brain and Mind, 9500 Gilman Dr, La Jolla, CA, USA
7Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
8Neurobiology Section, Division of Biological Sciences, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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Vikash Gilja
1Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
7Neurosciences Graduate Program, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, USA
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  • For correspondence: vgilja@eng.ucsd.edu
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Abstract

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 typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. 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 have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative 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 that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative 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 to common computational principles for learning and/or generating complex motor-vocal behaviors.

Author Summary Vocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

Competing Interest Statement

Vikash Gilja holds shares in Neuralink, Corp., and Paradromics, Inc., and currently consults for Paradromics, Inc.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 26, 2020.
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Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences
Daril E. Brown II, Jairo I. Chavez, Derek H. Nguyen, Adam Kadwory, Bradley Voytek, Ezequiel Arneodo, Timothy Q. Gentner, Vikash Gilja
bioRxiv 2020.06.30.179861; doi: https://doi.org/10.1101/2020.06.30.179861
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Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences
Daril E. Brown II, Jairo I. Chavez, Derek H. Nguyen, Adam Kadwory, Bradley Voytek, Ezequiel Arneodo, Timothy Q. Gentner, Vikash Gilja
bioRxiv 2020.06.30.179861; doi: https://doi.org/10.1101/2020.06.30.179861

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