Bifurcation analysis of the dynamics of interacting subnetworks of a spiking network

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Abstract: We analyze the collective dynamics of hierarchically structured networks of densely connected spiking neurons. These networks of sub-networks may represent interactions between cell assemblies or different nuclei in the brain. The dynamical activity pattern that results from these interactions depends on the strength of synaptic coupling between them. Importantly, the overall dynamics of a brain region in the absence of external input, so called ongoing brain activity, has been attributed to the dynamics of such interactions. In our study, two different network scenarios are considered: a system with one inhibitory and two excitatory subnetworks, and a network representation with three inhibitory subnetworks. To study the effect of synaptic strength on the global dynamics of the network, two parameters for relative couplings between these subnetworks are considered. For each case, a bifurcation analysis is performed and the results have been compared to large-scale network simulations. Our analysis shows that Generalized Lotka-Volterra (GLV) equations, well-known in predator-prey studies, yield a meaningful population-level description for the collective behavior of spiking neuronal interaction, which have a hierarchical structure. In particular, we observed a striking equivalence between the bifurcation diagrams of spiking neuronal networks and their corresponding GLV equations. This study gives new insight on the behavior of neuronal assemblies, and can potentially suggest new mechanisms for altering the dynamical patterns of spiking networks based on changing the synaptic strength between some groups of neurons

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch
Notes
Scientific reports. - 9 (2019) , 11397, ISSN: 2045-2322

Keyword
Angewandte Mathematik

Event
Veröffentlichung
(where)
Freiburg
(who)
Universität
(when)
2020
Creator
Lagzi, Fereshteh
Atay, Fatihcan M.
Rotter, Stefan

DOI
10.1038/s41598-019-47190-9
URN
urn:nbn:de:bsz:25-freidok-1516824
Rights
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Last update
25.03.2025, 1:48 PM CET

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Time of origin

  • 2020

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