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Switching Activity in an Ensemble of Excitable Neurons
Alexander G. Korotkova, Sergey Yu. Zagrebina, Elena Yu. Kadinaa, Grigory V. Osipovb a Department of Control Theory and Dynamics of Systems,
National Research Lobachevsky State University of Nizhny Novgorod,
pr. Gagarina 23, 603950 Nizhny Novgorod, Russia
b Research and Education Mathematics Centre “Mathematics for Future Technologies”,
pr. Gagarina 23, 603950 Nizhny Novgorod, Russia
Аннотация:
In [1], a stable heteroclinic cycle was proposed as a mathematical image of switching
activity. Due to the stability of the heteroclinic cycle, the sequential activity of the elements
of such a network is not limited in time. In this paper, it is proposed to use an unstable
heteroclinic cycle as a mathematical image of switching activity. We propose two dynamical
systems based on the generalized Lotka – Volterra model of three excitable elements interacting
through excitatory couplings. It is shown that in the space of coupling parameters there is a
region such that, when coupling parameters in this region are chosen, the phase space of systems
contains unstable heteroclinic cycles containing three or six saddles and heteroclinic trajectories
connecting them. Depending on the initial conditions, the phase trajectory will sequentially
visit the neighborhood of saddle equilibria (possibly more than once). The described behavior
is proposed to be used to simulate time-limited switching activity in neural ensembles. Different
transients are determined by different initial conditions. The passage of the phase point of the
system near the saddle equilibria included in the heteroclinic cycle is proposed to be interpreted
as activation of the corresponding element.
Ключевые слова:
neuron, excitable system, excitable coupling, heteroclinic cycles, sequential switching
activity
Поступила в редакцию: 27.11.2023 Принята в печать: 02.09.2024
Образец цитирования:
Alexander G. Korotkov, Sergey Yu. Zagrebin, Elena Yu. Kadina, Grigory V. Osipov, “Switching Activity in an Ensemble of Excitable Neurons”, Regul. Chaotic Dyn., 29:6 (2024), 886–900
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/rcd1289 https://www.mathnet.ru/rus/rcd/v29/i6/p886
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