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Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning
In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural plasticity for a very long time. Recently, neuronal intrinsic plasticity (IP) has become a hot topic in this area. IP is sometimes thought to be an information-maximization mechanism. However, it is st...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650036/ https://www.ncbi.nlm.nih.gov/pubmed/23671642 http://dx.doi.org/10.1371/journal.pone.0062894 |
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author | Li, Yuke Li, Chunguang |
author_facet | Li, Yuke Li, Chunguang |
author_sort | Li, Yuke |
collection | PubMed |
description | In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural plasticity for a very long time. Recently, neuronal intrinsic plasticity (IP) has become a hot topic in this area. IP is sometimes thought to be an information-maximization mechanism. However, it is still unclear how IP affects the performance of artificial neural networks in supervised learning applications. From an information-theoretical perspective, the error-entropy minimization (MEE) algorithm has newly been proposed as an efficient training method. In this study, we propose a synergistic learning algorithm combining the MEE algorithm as the synaptic plasticity rule and an information-maximization algorithm as the intrinsic plasticity rule. We consider both feedforward and recurrent neural networks and study the interactions between intrinsic and synaptic plasticity. Simulations indicate that the intrinsic plasticity rule can improve the performance of artificial neural networks trained by the MEE algorithm. |
format | Online Article Text |
id | pubmed-3650036 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36500362013-05-13 Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning Li, Yuke Li, Chunguang PLoS One Research Article In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural plasticity for a very long time. Recently, neuronal intrinsic plasticity (IP) has become a hot topic in this area. IP is sometimes thought to be an information-maximization mechanism. However, it is still unclear how IP affects the performance of artificial neural networks in supervised learning applications. From an information-theoretical perspective, the error-entropy minimization (MEE) algorithm has newly been proposed as an efficient training method. In this study, we propose a synergistic learning algorithm combining the MEE algorithm as the synaptic plasticity rule and an information-maximization algorithm as the intrinsic plasticity rule. We consider both feedforward and recurrent neural networks and study the interactions between intrinsic and synaptic plasticity. Simulations indicate that the intrinsic plasticity rule can improve the performance of artificial neural networks trained by the MEE algorithm. Public Library of Science 2013-05-09 /pmc/articles/PMC3650036/ /pubmed/23671642 http://dx.doi.org/10.1371/journal.pone.0062894 Text en © 2013 Li, Li http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Li, Yuke Li, Chunguang Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title | Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title_full | Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title_fullStr | Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title_full_unstemmed | Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title_short | Synergies between Intrinsic and Synaptic Plasticity Based on Information Theoretic Learning |
title_sort | synergies between intrinsic and synaptic plasticity based on information theoretic learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3650036/ https://www.ncbi.nlm.nih.gov/pubmed/23671642 http://dx.doi.org/10.1371/journal.pone.0062894 |
work_keys_str_mv | AT liyuke synergiesbetweenintrinsicandsynapticplasticitybasedoninformationtheoreticlearning AT lichunguang synergiesbetweenintrinsicandsynapticplasticitybasedoninformationtheoreticlearning |