Attention Enhances the Efficacy of Communication in V1 Local Circuits.


Journal

The Journal of neuroscience : the official journal of the Society for Neuroscience
ISSN: 1529-2401
Titre abrégé: J Neurosci
Pays: United States
ID NLM: 8102140

Informations de publication

Date de publication:
06 02 2019
Historique:
received: 22 08 2018
revised: 29 10 2018
accepted: 17 11 2018
pubmed: 14 12 2018
medline: 18 12 2019
entrez: 14 12 2018
Statut: ppublish

Résumé

Attention is a critical component of visual perception; however, the mechanisms of attention at the granular level are poorly understood. One possible mechanism by which attention modulates neuronal activity is to control the efficacy of communication between connected neurons; however, it is unclear whether attention alters communication efficacy across a variety of neuronal circuits. In parallel, attentional modulation of neuronal firing rate is not uniform but depends upon the match between neuronal feature selectivity and the feature required for successful task completion. Here we tested whether modulation of communication efficacy is a viable mechanism of attention by assessing whether it is consistent across a variety of neuronal circuits and dependent upon the type of information conveyed in each circuit. We identified monosynaptically connected pairs of V1 neurons through cross-correlation of neuronal spike trains recorded in adult female macaque monkeys performing attention-demanding contrast-change detection tasks. Attention toward the stimulus in the receptive field of recorded neurons significantly facilitated the efficacy of communication among connected pairs of V1 neurons. The amount of attentional enhancement depended upon neuronal physiology, with larger facilitation for circuits conveying information about task-relevant features. Furthermore, presynaptic activity was more determinant of attentional enhancement of communication efficacy than postsynaptic activity, and feedforward local circuits often displayed the largest facilitation with attention. Together, these findings highlight attentional modulation of communication efficacy as a generalized mechanism of attention and demonstrate that attentional modulation at the granular level depends on the relevance of feature-specific information conveyed by neuronal circuits.

Identifiants

pubmed: 30541911
pii: JNEUROSCI.2164-18.2018
doi: 10.1523/JNEUROSCI.2164-18.2018
pmc: PMC6363925
doi:

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

1066-1076

Subventions

Organisme : NEI NIH HHS
ID : F32 EY023165
Pays : United States
Organisme : NEI NIH HHS
ID : R00 EY018683
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY013588
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY025219
Pays : United States

Informations de copyright

Copyright © 2019 the authors 0270-6474/19/391066-11$15.00/0.

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Auteurs

Jacqueline R Hembrook-Short (JR)

Physiology & Neurobiology Department, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03756.

Vanessa L Mock (VL)

Physiology & Neurobiology Department, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03756.
Program in Experimental and Molecular Medicine, Dartmouth College, Hanover, New Hampshire 03755.
Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine, Rochester, New York 14642.

W Martin Usrey (WM)

Center for Neuroscience, University of California at Davis, Davis, California 95618.

Farran Briggs (F)

Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine, Rochester, New York 14642, farran_briggs@urmc.rochester.edu.
Department of Neuroscience, University of Rochester School of Medicine, Rochester, New York 14642.
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627, and.
Center for Visual Science, University of Rochester, Rochester, New York 14627.

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Classifications MeSH