Open AccessSynchronization in coupled neural network with inhibitory coupling
Author(s) -
А. В. Андреев,
Vladimir A. Maksimenko
Publication year - 2019
Publication title -
cybernetics and physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 8
eISSN - 2226-4116
pISSN - 2223-7038
DOI - 10.35470/2226-4116-2019-8-4-199-204
Subject(s) - subnetwork , biological neuron model , synchronization (alternating current) , artificial neural network , coupling (piping) , computer science , inhibitory postsynaptic potential , biological system , biological neural network , neuron , topology (electrical circuits) , network topology , physics , artificial intelligence , neuroscience , mathematics , machine learning , biology , operating system , mechanical engineering , computer security , combinatorics , engineering
A theoretical model of a network of neuron-like elements was constructed. The network included several subnetworks. The first subnetwork was used to translate a constant-amplitude signal into a spike sequence (conversion of amplitude to frequency). A similar process occurs in the brain when perceiving visual information. With an increase in the flow of information, the generation frequency of the neural ensemble participating in the processing increases. Further, the first subnetwork transmitted excitation to two large interconnected subnetworks. These subnetworks simulated the dynamics of the cortical neuronal populations. It was shown that in the presence of inhibitory coupling, the neuronal ensembles demonstrate antiphase dynamics. Various connectivity topologies and various types of neuron-like oscillators were investigated. We compare the results obtained in a discrete neuron model (Rulkov model) and a continuous-time model (Hodgkin-Huxley). It is shown that in the case of a discrete neuron model, the periodic dynamics is manifested in the alternate excitation of various neural ensembles. In the case of the continuous-time model, periodic modulation of the synchronization index of neural ensembles is observed.