Population adaptation in efficient balanced networks
Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a popul...
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doaj-069c21fc455140069e1b5ff13a771b642021-05-05T17:57:00ZengeLife Sciences Publications LtdeLife2050-084X2019-09-01810.7554/eLife.46926Population adaptation in efficient balanced networksGabrielle J Gutierrez0https://orcid.org/0000-0002-2350-1559Sophie Denève1Department of Applied Mathematics, University of Washington, Seattle, United States; Group for Neural Theory, École Normale Supérieure, Paris, FranceGroup for Neural Theory, École Normale Supérieure, Paris, FranceAdaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will redistribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.https://elifesciences.org/articles/46926visual encodingneural tuningperceptionefficient encodingspike-frequency adaptation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Gabrielle J Gutierrez Sophie Denève |
spellingShingle |
Gabrielle J Gutierrez Sophie Denève Population adaptation in efficient balanced networks eLife visual encoding neural tuning perception efficient encoding spike-frequency adaptation |
author_facet |
Gabrielle J Gutierrez Sophie Denève |
author_sort |
Gabrielle J Gutierrez |
title |
Population adaptation in efficient balanced networks |
title_short |
Population adaptation in efficient balanced networks |
title_full |
Population adaptation in efficient balanced networks |
title_fullStr |
Population adaptation in efficient balanced networks |
title_full_unstemmed |
Population adaptation in efficient balanced networks |
title_sort |
population adaptation in efficient balanced networks |
publisher |
eLife Sciences Publications Ltd |
series |
eLife |
issn |
2050-084X |
publishDate |
2019-09-01 |
description |
Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will redistribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost. |
topic |
visual encoding neural tuning perception efficient encoding spike-frequency adaptation |
url |
https://elifesciences.org/articles/46926 |
work_keys_str_mv |
AT gabriellejgutierrez populationadaptationinefficientbalancednetworks AT sophiedeneve populationadaptationinefficientbalancednetworks |
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1721458815015059456 |