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Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome

Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes...

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Autores principales: Morales, Alejandro, Froese, Tom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805867/
https://www.ncbi.nlm.nih.gov/pubmed/33501208
http://dx.doi.org/10.3389/frobt.2020.00040
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author Morales, Alejandro
Froese, Tom
author_facet Morales, Alejandro
Froese, Tom
author_sort Morales, Alejandro
collection PubMed
description Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneficial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process.
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spelling pubmed-78058672021-01-25 Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome Morales, Alejandro Froese, Tom Front Robot AI Robotics and AI Modeling of complex adaptive systems has revealed a still poorly understood benefit of unsupervised learning: when neural networks are enabled to form an associative memory of a large set of their own attractor configurations, they begin to reorganize their connectivity in a direction that minimizes the coordination constraints posed by the initial network architecture. This self-optimization process has been replicated in various neural network formalisms, but it is still unclear whether it can be applied to biologically more realistic network topologies and scaled up to larger networks. Here we continue our efforts to respond to these challenges by demonstrating the process on the connectome of the widely studied nematode worm C. elegans. We extend our previous work by considering the contributions made by hierarchical partitions of the connectome that form functional clusters, and we explore possible beneficial effects of inter-cluster inhibitory connections. We conclude that the self-optimization process can be applied to neural network topologies characterized by greater biological realism, and that long-range inhibitory connections can facilitate the generalization capacity of the process. Frontiers Media S.A. 2020-04-02 /pmc/articles/PMC7805867/ /pubmed/33501208 http://dx.doi.org/10.3389/frobt.2020.00040 Text en Copyright © 2020 Morales and Froese. 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 Robotics and AI
Morales, Alejandro
Froese, Tom
Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title_full Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title_fullStr Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title_full_unstemmed Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title_short Unsupervised Learning Facilitates Neural Coordination Across the Functional Clusters of the C. elegans Connectome
title_sort unsupervised learning facilitates neural coordination across the functional clusters of the c. elegans connectome
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805867/
https://www.ncbi.nlm.nih.gov/pubmed/33501208
http://dx.doi.org/10.3389/frobt.2020.00040
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