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Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations
Many cognitive and behavioral tasks—such as interval timing, spatial navigation, motor control, and speech—require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505196/ https://www.ncbi.nlm.nih.gov/pubmed/33013342 http://dx.doi.org/10.3389/fncom.2020.00078 |
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author | Vincent-Lamarre, Philippe Calderini, Matias Thivierge, Jean-Philippe |
author_facet | Vincent-Lamarre, Philippe Calderini, Matias Thivierge, Jean-Philippe |
author_sort | Vincent-Lamarre, Philippe |
collection | PubMed |
description | Many cognitive and behavioral tasks—such as interval timing, spatial navigation, motor control, and speech—require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain. |
format | Online Article Text |
id | pubmed-7505196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75051962020-10-02 Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations Vincent-Lamarre, Philippe Calderini, Matias Thivierge, Jean-Philippe Front Comput Neurosci Neuroscience Many cognitive and behavioral tasks—such as interval timing, spatial navigation, motor control, and speech—require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain. Frontiers Media S.A. 2020-09-07 /pmc/articles/PMC7505196/ /pubmed/33013342 http://dx.doi.org/10.3389/fncom.2020.00078 Text en Copyright © 2020 Vincent-Lamarre, Calderini and Thivierge. http://creativecommons.org/licenses/by/4.0/ 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) and the copyright owner(s) 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 Vincent-Lamarre, Philippe Calderini, Matias Thivierge, Jean-Philippe Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title | Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title_full | Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title_fullStr | Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title_full_unstemmed | Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title_short | Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations |
title_sort | learning long temporal sequences in spiking networks by multiplexing neural oscillations |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7505196/ https://www.ncbi.nlm.nih.gov/pubmed/33013342 http://dx.doi.org/10.3389/fncom.2020.00078 |
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