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 hypernetwork 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 hypernetwork in the form of a random walk....

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Published inPhilosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 375; no. 2096; p. 20160288
Main Authors Maslennikov, Oleg V., Shchapin, Dmitry S., Nekorkin, Vladimir I.
Format Journal Article
LanguageEnglish
Published England The Royal Society Publishing 28.06.2017
Subjects
Action Potentials - physiology
Adaptation, Physiological - physiology
Adaptive Network
Animals
Brain - physiology
Cardiology
Cluster State
Clustering
Computer Simulation
Connectome - methods
Humans
Hypernetwork
Map-Based Model
Models, Neurological
Models, Statistical
Nerve Net - physiology
Neuronal Plasticity - physiology
Neurons
Neurons - physiology
Random walk
Sequences
Spiking
Spiking Neuron
Transient Sequence
spiking neuron
transient sequence
cluster state
hypernetwork
map-based model
adaptive network
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Summary:We propose a model of an adaptive network of spiking neurons that gives rise to a hypernetwork 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 hypernetwork in the form of a random walk. Receiving inputs the system is able to generate reproducible sequences corresponding to stimulus-specific paths in the hypernetwork. 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'.
Bibliography:Theme issue “Mathematical methods in medicine: neuroscience, cardiology and pathology” compiled and edited by José M. Amigó and Michael Small
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SourceType-Scholarly Journals-1
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One contribution of 14 to a theme issue ‘Mathematical methods in medicine: neuroscience, cardiology and pathology’.
ISSN:1364-503X
1471-2962
DOI:10.1098/rsta.2016.0288