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Selective connectivity enhances storage capacity in attractor models of memory function
Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, thei...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519847/ https://www.ncbi.nlm.nih.gov/pubmed/36185821 http://dx.doi.org/10.3389/fnsys.2022.983147 |
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author | Emina, Facundo Kropff, Emilio |
author_facet | Emina, Facundo Kropff, Emilio |
author_sort | Emina, Facundo |
collection | PubMed |
description | Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity. |
format | Online Article Text |
id | pubmed-9519847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95198472022-09-30 Selective connectivity enhances storage capacity in attractor models of memory function Emina, Facundo Kropff, Emilio Front Syst Neurosci Neuroscience Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity. Frontiers Media S.A. 2022-09-15 /pmc/articles/PMC9519847/ /pubmed/36185821 http://dx.doi.org/10.3389/fnsys.2022.983147 Text en Copyright © 2022 Emina and Kropff. https://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 Emina, Facundo Kropff, Emilio Selective connectivity enhances storage capacity in attractor models of memory function |
title | Selective connectivity enhances storage capacity in attractor models of memory function |
title_full | Selective connectivity enhances storage capacity in attractor models of memory function |
title_fullStr | Selective connectivity enhances storage capacity in attractor models of memory function |
title_full_unstemmed | Selective connectivity enhances storage capacity in attractor models of memory function |
title_short | Selective connectivity enhances storage capacity in attractor models of memory function |
title_sort | selective connectivity enhances storage capacity in attractor models of memory function |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519847/ https://www.ncbi.nlm.nih.gov/pubmed/36185821 http://dx.doi.org/10.3389/fnsys.2022.983147 |
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