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Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for l...

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Detalles Bibliográficos
Autores principales: Maes, Amadeus, Barahona, Mauricio, Clopath, Claudia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023498/
https://www.ncbi.nlm.nih.gov/pubmed/33764970
http://dx.doi.org/10.1371/journal.pcbi.1008866
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author Maes, Amadeus
Barahona, Mauricio
Clopath, Claudia
author_facet Maes, Amadeus
Barahona, Mauricio
Clopath, Claudia
author_sort Maes, Amadeus
collection PubMed
description Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.
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spelling pubmed-80234982021-04-15 Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons Maes, Amadeus Barahona, Mauricio Clopath, Claudia PLoS Comput Biol Research Article Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings. Public Library of Science 2021-03-25 /pmc/articles/PMC8023498/ /pubmed/33764970 http://dx.doi.org/10.1371/journal.pcbi.1008866 Text en © 2021 Maes et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Maes, Amadeus
Barahona, Mauricio
Clopath, Claudia
Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title_full Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title_fullStr Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title_full_unstemmed Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title_short Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
title_sort learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023498/
https://www.ncbi.nlm.nih.gov/pubmed/33764970
http://dx.doi.org/10.1371/journal.pcbi.1008866
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