Attention stabilizes the shared gain of V4 populations
Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population...
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Online Access: | https://elifesciences.org/articles/08998 |
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doaj-a574cbaed8724dff9c5c5a779ec7dfc92021-05-05T00:05:43ZengeLife Sciences Publications LtdeLife2050-084X2015-11-01410.7554/eLife.08998Attention stabilizes the shared gain of V4 populationsNeil C Rabinowitz0Robbe L Goris1Marlene Cohen2Eero P Simoncelli3Center for Neural Science, Howard Hughes Medical Institute, New York University, New York, United StatesCenter for Neural Science, Howard Hughes Medical Institute, New York University, New York, United StatesDepartment of Neuroscience and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, United StatesCenter for Neural Science, Howard Hughes Medical Institute, New York University, New York, United StatesResponses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior.https://elifesciences.org/articles/08998computationsensoryvisionstatisticattention |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Neil C Rabinowitz Robbe L Goris Marlene Cohen Eero P Simoncelli |
spellingShingle |
Neil C Rabinowitz Robbe L Goris Marlene Cohen Eero P Simoncelli Attention stabilizes the shared gain of V4 populations eLife computation sensory vision statistic attention |
author_facet |
Neil C Rabinowitz Robbe L Goris Marlene Cohen Eero P Simoncelli |
author_sort |
Neil C Rabinowitz |
title |
Attention stabilizes the shared gain of V4 populations |
title_short |
Attention stabilizes the shared gain of V4 populations |
title_full |
Attention stabilizes the shared gain of V4 populations |
title_fullStr |
Attention stabilizes the shared gain of V4 populations |
title_full_unstemmed |
Attention stabilizes the shared gain of V4 populations |
title_sort |
attention stabilizes the shared gain of v4 populations |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2015-11-01 |
description |
Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior. |
topic |
computation sensory vision statistic attention |
url |
https://elifesciences.org/articles/08998 |
work_keys_str_mv |
AT neilcrabinowitz attentionstabilizesthesharedgainofv4populations AT robbelgoris attentionstabilizesthesharedgainofv4populations AT marlenecohen attentionstabilizesthesharedgainofv4populations AT eeropsimoncelli attentionstabilizesthesharedgainofv4populations |
_version_ |
1721476646009044992 |