Transient sequences in a hypernetwork generated by an adaptive network of spiking neurons

We propose a model of an adaptive network of spiking neurons that gives rise to a hypemetwork of its dynamic states at the upper level of description. Left to itself, the network exhibits a sequence of transient clustering which relates to a traffic in the hypemetwork in the form of a random walk. R...

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Published inPhilosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 375; no. 2096; pp. 1 - 12
Main Authors Maslennikov, Oleg V., Shchapin, Dmitry S., Nekorkin, Vladimir I.
Format Journal Article
LanguageEnglish
Published THE ROYAL SOCIETY 28.06.2017
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Summary:We propose a model of an adaptive network of spiking neurons that gives rise to a hypemetwork of its dynamic states at the upper level of description. Left to itself, the network exhibits a sequence of transient clustering which relates to a traffic in the hypemetwork in the form of a random walk. Receiving inputs the system is able to generate reproducible sequences corresponding to stimulusspecific paths in the hypemetwork. We illustrate these basic notions by a simple network of discrete-time spiking neurons together with its FPGA realization and analyse their properties. This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.
ISSN:1364-503X
1471-2962