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Rapid learning in visual cortical networks

Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of m...

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Detalles Bibliográficos
Autores principales: Wang, Ye, Dragoi, Valentin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588715/
https://www.ncbi.nlm.nih.gov/pubmed/26308578
http://dx.doi.org/10.7554/eLife.08417
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author Wang, Ye
Dragoi, Valentin
author_facet Wang, Ye
Dragoi, Valentin
author_sort Wang, Ye
collection PubMed
description Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning. DOI: http://dx.doi.org/10.7554/eLife.08417.001
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spelling pubmed-45887152015-10-01 Rapid learning in visual cortical networks Wang, Ye Dragoi, Valentin eLife Neuroscience Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning. DOI: http://dx.doi.org/10.7554/eLife.08417.001 eLife Sciences Publications, Ltd 2015-08-26 /pmc/articles/PMC4588715/ /pubmed/26308578 http://dx.doi.org/10.7554/eLife.08417 Text en © 2015, Wang and Dragoi http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Wang, Ye
Dragoi, Valentin
Rapid learning in visual cortical networks
title Rapid learning in visual cortical networks
title_full Rapid learning in visual cortical networks
title_fullStr Rapid learning in visual cortical networks
title_full_unstemmed Rapid learning in visual cortical networks
title_short Rapid learning in visual cortical networks
title_sort rapid learning in visual cortical networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588715/
https://www.ncbi.nlm.nih.gov/pubmed/26308578
http://dx.doi.org/10.7554/eLife.08417
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