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Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses


Zendrikov, Dmitrii; Paraskevov, Alexander (2021). Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses. Neurocomputing, 461:727-742.

Abstract

We show that networks of excitatory neurons with stochastic spontaneous spiking activity and short-term synaptic plasticity can exhibit spontaneous repetitive synchronization in so-called population spikes. The major reason for this is that synaptic plasticity nonlinearly modulates the interaction between neurons. For large-scale two-dimensional networks, where the connection probability decreases exponentially with increasing distance between the neurons resulting in a small-world network connectome, a population spike occurs in the form of circular traveling waves diverging from seemingly non-stationary nucleation sites. The latter is in drastic contrast to the case of networks with a fixed fraction of steady pacemaker neurons, where the set of a few spontaneously formed nucleation sites is stationary. Despite the spatial non-stationarity of their nucleation, population spikes may occur surprisingly regularly. From a theoretical viewpoint, these findings show that the regime of nearly-periodic population spikes, which mimics respiratory rhythm, can occur strictly without stochastic resonance. In addition, the observed spatiotemporal effects serve as an example of transient chimera patterns.

Abstract

We show that networks of excitatory neurons with stochastic spontaneous spiking activity and short-term synaptic plasticity can exhibit spontaneous repetitive synchronization in so-called population spikes. The major reason for this is that synaptic plasticity nonlinearly modulates the interaction between neurons. For large-scale two-dimensional networks, where the connection probability decreases exponentially with increasing distance between the neurons resulting in a small-world network connectome, a population spike occurs in the form of circular traveling waves diverging from seemingly non-stationary nucleation sites. The latter is in drastic contrast to the case of networks with a fixed fraction of steady pacemaker neurons, where the set of a few spontaneously formed nucleation sites is stationary. Despite the spatial non-stationarity of their nucleation, population spikes may occur surprisingly regularly. From a theoretical viewpoint, these findings show that the regime of nearly-periodic population spikes, which mimics respiratory rhythm, can occur strictly without stochastic resonance. In addition, the observed spatiotemporal effects serve as an example of transient chimera patterns.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Computer Science Applications
Life Sciences > Cognitive Neuroscience
Physical Sciences > Artificial Intelligence
Uncontrolled Keywords:Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications
Language:English
Date:1 October 2021
Deposited On:16 Mar 2022 10:58
Last Modified:27 Apr 2024 01:36
Publisher:Elsevier
ISSN:0925-2312
OA Status:Green
Publisher DOI:https://doi.org/10.1016/j.neucom.2020.11.073
Project Information:
  • : FunderUniversity of Cambridge
  • : Grant ID
  • : Project Title
  • : FunderWellcome Trust
  • : Grant ID
  • : Project Title
  • Content: Accepted Version