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Controlling neural network responsiveness: tradeoffs and constraints

In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole br...

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Detalles Bibliográficos
Autores principales: Keren, Hanna, Marom, Shimon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010759/
https://www.ncbi.nlm.nih.gov/pubmed/24808860
http://dx.doi.org/10.3389/fneng.2014.00011
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author Keren, Hanna
Marom, Shimon
author_facet Keren, Hanna
Marom, Shimon
author_sort Keren, Hanna
collection PubMed
description In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input–output relations and activity dependent dispersion of neuronal pair-wise correlations across the network.
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spelling pubmed-40107592014-05-07 Controlling neural network responsiveness: tradeoffs and constraints Keren, Hanna Marom, Shimon Front Neuroeng Neuroscience In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input–output relations and activity dependent dispersion of neuronal pair-wise correlations across the network. Frontiers Media S.A. 2014-04-29 /pmc/articles/PMC4010759/ /pubmed/24808860 http://dx.doi.org/10.3389/fneng.2014.00011 Text en Copyright © 2014 Keren and Marom. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Keren, Hanna
Marom, Shimon
Controlling neural network responsiveness: tradeoffs and constraints
title Controlling neural network responsiveness: tradeoffs and constraints
title_full Controlling neural network responsiveness: tradeoffs and constraints
title_fullStr Controlling neural network responsiveness: tradeoffs and constraints
title_full_unstemmed Controlling neural network responsiveness: tradeoffs and constraints
title_short Controlling neural network responsiveness: tradeoffs and constraints
title_sort controlling neural network responsiveness: tradeoffs and constraints
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4010759/
https://www.ncbi.nlm.nih.gov/pubmed/24808860
http://dx.doi.org/10.3389/fneng.2014.00011
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