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Computing with Neural Synchrony
Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candid...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2012
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375225/ https://www.ncbi.nlm.nih.gov/pubmed/22719243 http://dx.doi.org/10.1371/journal.pcbi.1002561 |
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author | Brette, Romain |
author_facet | Brette, Romain |
author_sort | Brette, Romain |
collection | PubMed |
description | Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. |
format | Online Article Text |
id | pubmed-3375225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33752252012-06-20 Computing with Neural Synchrony Brette, Romain PLoS Comput Biol Research Article Neurons communicate primarily with spikes, but most theories of neural computation are based on firing rates. Yet, many experimental observations suggest that the temporal coordination of spikes plays a role in sensory processing. Among potential spike-based codes, synchrony appears as a good candidate because neural firing and plasticity are sensitive to fine input correlations. However, it is unclear what role synchrony may play in neural computation, and what functional advantage it may provide. With a theoretical approach, I show that the computational interest of neural synchrony appears when neurons have heterogeneous properties. In this context, the relationship between stimuli and neural synchrony is captured by the concept of synchrony receptive field, the set of stimuli which induce synchronous responses in a group of neurons. In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. This theory of synchrony-based computation shows that relative spike timing may indeed have computational relevance, and suggests new types of neural network models for sensory processing with appealing computational properties. Public Library of Science 2012-06-14 /pmc/articles/PMC3375225/ /pubmed/22719243 http://dx.doi.org/10.1371/journal.pcbi.1002561 Text en Romain Brette. 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 Brette, Romain Computing with Neural Synchrony |
title | Computing with Neural Synchrony |
title_full | Computing with Neural Synchrony |
title_fullStr | Computing with Neural Synchrony |
title_full_unstemmed | Computing with Neural Synchrony |
title_short | Computing with Neural Synchrony |
title_sort | computing with neural synchrony |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3375225/ https://www.ncbi.nlm.nih.gov/pubmed/22719243 http://dx.doi.org/10.1371/journal.pcbi.1002561 |
work_keys_str_mv | AT bretteromain computingwithneuralsynchrony |