Evolution-Communication Spiking Neural P Systems

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules...

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Vydáno v:International journal of neural systems Ročník 31; číslo 2; s. 2050064
Hlavní autoři: Wu, Tingfang, Lyu, Qiang, Pan, Linqiang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore 01.02.2021
Témata:
computational power
Membrane computing
spiking neural P system
spiking neural network
neural computation
ISSN:1793-6462, 1793-6462
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Shrnutí:Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons and the distribution of produced spikes, and a novel variant of the SNP systems, named evolution-communication SNP (ECSNP) systems, is proposed. More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power.
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ISSN:1793-6462
1793-6462
DOI:10.1142/S0129065720500641