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Mechanisms of self-sustained oscillatory states in hierarchical modular networks with mixtures of electrophysiological cell types

  • In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics "up" and "down" states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.

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Author:Petar Tomov, Rodrigo F. O. Pena, Antonio C. Roque, Michael A. ZaksORCiDGND
Parent Title (English):Frontiers in computational neuroscience
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (452)
Document Type:Postprint
Date of first Publication:2018/06/28
Year of Completion:2016
Publishing Institution:Universität Potsdam
Release Date:2018/06/28
Tag:chaotic neural dynamics; cortical network models; cortical oscillations; hierarchical modular networks; intrinsic neuronal diversity; irregular firing activity; self-sustained activity; up-down states
Source:Frontiers in computational neuroscience 10 (2016), Art. 23 ; DOI: 10.3389/fncom.2016.00023
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer Review:Referiert
Publication Way:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, 4.0 International