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Learning structure of sensory inputs with synaptic plasticity leads to interference
Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525052/ https://www.ncbi.nlm.nih.gov/pubmed/26300769 http://dx.doi.org/10.3389/fncom.2015.00103 |
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author | Chrol-Cannon, Joseph Jin, Yaochu |
author_facet | Chrol-Cannon, Joseph Jin, Yaochu |
author_sort | Chrol-Cannon, Joseph |
collection | PubMed |
description | Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience. |
format | Online Article Text |
id | pubmed-4525052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-45250522015-08-21 Learning structure of sensory inputs with synaptic plasticity leads to interference Chrol-Cannon, Joseph Jin, Yaochu Front Comput Neurosci Neuroscience Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience. Frontiers Media S.A. 2015-08-05 /pmc/articles/PMC4525052/ /pubmed/26300769 http://dx.doi.org/10.3389/fncom.2015.00103 Text en Copyright © 2015 Chrol-Cannon and Jin. 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) or licensor 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 Chrol-Cannon, Joseph Jin, Yaochu Learning structure of sensory inputs with synaptic plasticity leads to interference |
title | Learning structure of sensory inputs with synaptic plasticity leads to interference |
title_full | Learning structure of sensory inputs with synaptic plasticity leads to interference |
title_fullStr | Learning structure of sensory inputs with synaptic plasticity leads to interference |
title_full_unstemmed | Learning structure of sensory inputs with synaptic plasticity leads to interference |
title_short | Learning structure of sensory inputs with synaptic plasticity leads to interference |
title_sort | learning structure of sensory inputs with synaptic plasticity leads to interference |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525052/ https://www.ncbi.nlm.nih.gov/pubmed/26300769 http://dx.doi.org/10.3389/fncom.2015.00103 |
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