Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model

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• dc.contributor.author Gigante, Guido
• dc.contributor.author Deco, Gustavo
• dc.contributor.author Marom, Shimon
• dc.contributor.author Del Giudice, Paolo
• dc.date.accessioned 2019-04-11T09:20:18Z
• dc.date.available 2019-04-11T09:20:18Z
• dc.date.issued 2015
• dc.description.abstract Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed ‘quasi-orbits’, which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network’s firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.
• dc.description.sponsorship We aknowledge the support of EU-Project CORONET (FP7-ICT-269459). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• dc.format.mimetype application/pdf
• dc.identifier.citation Gigante G, Deco G, Marom S, Del Giudice P. Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model. PLoS Comput Biol. 2015 Nov 11;11:e1004547. DOI: 10.1371/journal.pcbi.1004547
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1004547
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/37091
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLOS Computational Biology. 2015 Nov 11;11:e1004547
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269459
• dc.rights © 2015 Gigante et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.title Network events on multiple space and time scales in cultured neural networks and in a stochastic rate model
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion