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Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem...

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Detalles Bibliográficos
Autores principales: Pang, Rich, Fairhall, Adrienne L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538377/
https://www.ncbi.nlm.nih.gov/pubmed/31081753
http://dx.doi.org/10.7554/eLife.44324
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author Pang, Rich
Fairhall, Adrienne L
author_facet Pang, Rich
Fairhall, Adrienne L
author_sort Pang, Rich
collection PubMed
description Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to ‘replay’ during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic ‘gating’ inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE ‘tags’ specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility.
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spelling pubmed-65383772019-05-29 Fast and flexible sequence induction in spiking neural networks via rapid excitability changes Pang, Rich Fairhall, Adrienne L eLife Computational and Systems Biology Cognitive flexibility likely depends on modulation of the dynamics underlying how biological neural networks process information. While dynamics can be reshaped by gradually modifying connectivity, less is known about mechanisms operating on faster timescales. A compelling entrypoint to this problem is the observation that exploratory behaviors can rapidly cause selective hippocampal sequences to ‘replay’ during rest. Using a spiking network model, we asked whether simplified replay could arise from three biological components: fixed recurrent connectivity; stochastic ‘gating’ inputs; and rapid gating input scaling via long-term potentiation of intrinsic excitability (LTP-IE). Indeed, these enabled both forward and reverse replay of recent sensorimotor-evoked sequences, despite unchanged recurrent weights. LTP-IE ‘tags’ specific neurons with increased spiking probability under gating input, and ordering is reconstructed from recurrent connectivity. We further show how LTP-IE can implement temporary stimulus-response mappings. This elucidates a novel combination of mechanisms that might play a role in rapid cognitive flexibility. eLife Sciences Publications, Ltd 2019-05-13 /pmc/articles/PMC6538377/ /pubmed/31081753 http://dx.doi.org/10.7554/eLife.44324 Text en © 2019, Pang and Fairhall http://creativecommons.org/licenses/by/4.0/ 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 Computational and Systems Biology
Pang, Rich
Fairhall, Adrienne L
Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title_full Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title_fullStr Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title_full_unstemmed Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title_short Fast and flexible sequence induction in spiking neural networks via rapid excitability changes
title_sort fast and flexible sequence induction in spiking neural networks via rapid excitability changes
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538377/
https://www.ncbi.nlm.nih.gov/pubmed/31081753
http://dx.doi.org/10.7554/eLife.44324
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