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Turing complete neural computation based on synaptic plasticity
In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the cruc...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795493/ https://www.ncbi.nlm.nih.gov/pubmed/31618230 http://dx.doi.org/10.1371/journal.pone.0223451 |
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author | Cabessa, Jérémie |
author_facet | Cabessa, Jérémie |
author_sort | Cabessa, Jérémie |
collection | PubMed |
description | In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms. More specifically, we prove that any 2-counter machine—and hence any Turing machine—can be simulated by a rational-weighted recurrent neural network employing spike-timing-dependent plasticity (STDP) rules. The computational states and counter values of the machine are encoded into discrete synaptic strengths. The transitions between those synaptic weights are then achieved via STDP. These considerations show that a Turing complete synaptic-based paradigm of neural computation is theoretically possible and potentially exploitable. They support the idea that synapses are not only crucially involved in information processing and learning features, but also in the encoding of essential information. This approach represents a paradigm shift in the field of neural computation. |
format | Online Article Text |
id | pubmed-6795493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67954932019-10-20 Turing complete neural computation based on synaptic plasticity Cabessa, Jérémie PLoS One Research Article In neural computation, the essential information is generally encoded into the neurons via their spiking configurations, activation values or (attractor) dynamics. The synapses and their associated plasticity mechanisms are, by contrast, mainly used to process this information and implement the crucial learning features. Here, we propose a novel Turing complete paradigm of neural computation where the essential information is encoded into discrete synaptic states, and the updating of this information achieved via synaptic plasticity mechanisms. More specifically, we prove that any 2-counter machine—and hence any Turing machine—can be simulated by a rational-weighted recurrent neural network employing spike-timing-dependent plasticity (STDP) rules. The computational states and counter values of the machine are encoded into discrete synaptic strengths. The transitions between those synaptic weights are then achieved via STDP. These considerations show that a Turing complete synaptic-based paradigm of neural computation is theoretically possible and potentially exploitable. They support the idea that synapses are not only crucially involved in information processing and learning features, but also in the encoding of essential information. This approach represents a paradigm shift in the field of neural computation. Public Library of Science 2019-10-16 /pmc/articles/PMC6795493/ /pubmed/31618230 http://dx.doi.org/10.1371/journal.pone.0223451 Text en © 2019 Jérémie Cabessa http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cabessa, Jérémie Turing complete neural computation based on synaptic plasticity |
title | Turing complete neural computation based on synaptic plasticity |
title_full | Turing complete neural computation based on synaptic plasticity |
title_fullStr | Turing complete neural computation based on synaptic plasticity |
title_full_unstemmed | Turing complete neural computation based on synaptic plasticity |
title_short | Turing complete neural computation based on synaptic plasticity |
title_sort | turing complete neural computation based on synaptic plasticity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6795493/ https://www.ncbi.nlm.nih.gov/pubmed/31618230 http://dx.doi.org/10.1371/journal.pone.0223451 |
work_keys_str_mv | AT cabessajeremie turingcompleteneuralcomputationbasedonsynapticplasticity |