ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


For citation:

Bazhanova M. V., Gordleeva S. J., Kazantsev V. B., Lobov S. А. Control of network bursting discharges by local electrical stimulation in spiking neuron network. Izvestiya VUZ. Applied Nonlinear Dynamics, 2021, vol. 29, iss. 3, pp. 428-439. DOI: 10.18500/0869-6632-2021-29-3-428-439

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
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English
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Article
UDC: 
530.182

Control of network bursting discharges by local electrical stimulation in spiking neuron network

Autors: 
Bazhanova Milena Viktorovna, Federal state budgetary educational institution of higher professional education Nizhny Novgorod state University named N. And.Lobachevsky
Gordleeva Susanna Jurevna, Federal state budgetary educational institution of higher professional education Nizhny Novgorod state University named N. And.Lobachevsky
Kazantsev Viktor Borisovich, Institute of Applied Physics of the Russian Academy of Sciences
Lobov Sergey Анатольевич, Federal state budgetary educational institution of higher professional education Nizhny Novgorod state University named N. And.Lobachevsky
Abstract: 

Goal. The paper is devoted to controlling the dynamics of spike neural networks by local periodic stimulation of various network sections. Methods. The simulation uses a network of synaptically connected spike neurons distributed in two-dimensional space. The dynamics of the transmembrane potential of neurons is described by the Izhikevich model, short-term synaptic plasticity is represented by the model Tsodyksa–Markram, the effects of changes in the efficiency of connections between neurons are modeled using spike-timing-dependent plasticity (STDP). Results. It is shown that the model reproduces the dynamics of living neural networks grown under in vitro conditions quite well. In its spontaneous dynamics, such a network exhibits a wide range of dynamic modes, including asynchronous spikes and quasi-synchronous spike bundles. It was found that due to STDP, the network can adapt to the stimulating signal, so that the network bundles become synchronized (phase-synchronized) with the stimulation signal. The analysis of the dependence of this effect on the parameters of stimulation, in particular, on the geometric dimensions of the stimulated area, as well as the connectivity of the network. Conclusion. With the help of local periodic stimulation of a part of the neural network,when selecting certain parameters of the stimulating signal, taking into account the characteristics of the network, externalcontrol of the dynamics of the spike neural network is possible.

Acknowledgments: 
The work was supported by Center for Technologies in Robotics and Mechatronics Components, Innopolis University
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Received: 
14.11.2020
Accepted: 
28.01.2021
Published: 
31.05.2021