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Turing Complete Neural Computation Based on Synaptic Plasticity
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SYSNO ASEP 0510528 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Turing Complete Neural Computation Based on Synaptic Plasticity Tvůrce(i) Cabessa, Jérémie (UIVT-O) ORCID Číslo článku e0223451 Zdroj.dok. PLoS ONE. - : Public Library of Science - ISSN 1932-6203
Roč. 14, č. 10 (2019)Poč.str. 34 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Action potentials ; Language ; Machine learning algorithms ; Neurons ; Recurrent neural networks ; Synapses ; Synaptic plasticity ; Neural pathways Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA19-05704S GA ČR - Grantová agentura ČR Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000532566600029 EID SCOPUS 85073468063 DOI 10.1371/journal.pone.0223451 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://hdl.handle.net/11104/0300985
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