GENERATION OF BURSTS IN ENSEMBLES OF SPIKING NEURONS WITH NONLOCAL COUPLING
Cite this article as:
Ivanchenko М. V. GENERATION OF BURSTS IN ENSEMBLES OF SPIKING NEURONS WITH NONLOCAL COUPLING. Izvestiya VUZ. Applied Nonlinear Dynamics, 2007, vol. 15, iss. 3, pp. 3-14. DOI: https://doi.org/10.18500/0869-6632-2007-15-3-3-14
A mechanism of collective generation of bursts in ensembles of spiking neurons with nonlocal excitatory coupling is studied. Three types of the network topology is considered: (a) chains with regular short-range nonlocal coupling, (b) chains with a small number of random long-range connections and dominating regular short-range ones, (c) random ensembles with a power law of node degree distribution. It is shown, that there exists a common mechanism of burst generation resulting from instability of synchronous slow spiking as the coupling strengthens, giving rise to fast repetitive spikes. Dependence upon parameters of the network is analyzed. The relevance of the obtained results to neuroscience is discussed.
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BibTeX
author = {М. V. Ivanchenko},
title = {GENERATION OF BURSTS IN ENSEMBLES OF SPIKING NEURONS WITH NONLOCAL COUPLING},
year = {2007},
journal = {Izvestiya VUZ. Applied Nonlinear Dynamics},
volume = {15},number = {3},
url = {https://old-andjournal.sgu.ru/en/articles/generation-of-bursts-in-ensembles-of-spiking-neurons-with-nonlocal-coupling},
address = {Саратов},
language = {russian},
doi = {10.18500/0869-6632-2007-15-3-3-14},pages = {3--14},issn = {0869-6632},
keywords = {-},
abstract = {A mechanism of collective generation of bursts in ensembles of spiking neurons with nonlocal excitatory coupling is studied. Three types of the network topology is considered: (a) chains with regular short-range nonlocal coupling, (b) chains with a small number of random long-range connections and dominating regular short-range ones, (c) random ensembles with a power law of node degree distribution. It is shown, that there exists a common mechanism of burst generation resulting from instability of synchronous slow spiking as the coupling strengthens, giving rise to fast repetitive spikes. Dependence upon parameters of the network is analyzed. The relevance of the obtained results to neuroscience is discussed. }}