40772
2016
2018
eng
17
postprint
1
2018-06-28
2018-06-28
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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.
Frontiers in computational neuroscience
urn:nbn:de:kobv:517-opus4-407724
online registration
Frontiers in computational neuroscience 10 (2016), Art. 23 ; DOI: 10.3389/fncom.2016.00023
CC-BY - Namensnennung 4.0 International
Petar Tomov
Rodrigo F. O. Pena
Antonio C. Roque
Michael A. Zaks
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
452
eng
uncontrolled
self-sustained activity
eng
uncontrolled
cortical oscillations
eng
uncontrolled
irregular firing activity
eng
uncontrolled
hierarchical modular networks
eng
uncontrolled
cortical network models
eng
uncontrolled
intrinsic neuronal diversity
eng
uncontrolled
up-down states
eng
uncontrolled
chaotic neural dynamics
Medizin und Gesundheit
open_access
Mathematisch-Naturwissenschaftliche Fakultät
Institut für Physik und Astronomie
Referiert
Open Access
Frontiers
Universität Potsdam
https://publishup.uni-potsdam.de/files/40772/pmnr_452.online.pdf
45510
2016
2016
eng
476
+
17
10
article
Frontiers Research Foundation
Lausanne
HESS Collaboration
1
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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.
Frontiers in computational neuroscience / Frontiers Research Foundation
10.3389/fncom.2016.00023
27047367
1662-5188
wos2016:2019
23
WOS:000372709900001
Roque, AC (reprint author), Univ Sao Paulo, Dept Phys, Sch Philosophy Sci & Letters Ribeirao Preto, Lab Sistemas Neurais, Sao Paulo, Brazil., antonior@ffciro.usp.br
IRTG [1740/TRP 2011/50150-0]; DFG/FAPESP; Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant [2013/07699-0]; FAPESP scholarship [2013/25667-8, 2015/09916-3]; CNPq research grant [306251/2014-0]; DFG [PI 220/17-1]
importub
2020-03-22T19:05:02+00:00
filename=package.tar
b0ac94c2ed658dad86e5fd5cb79fb41c
Peter Tomov
Rodrigo F. O. Pena
Antonio C. Roque
Michael A. Zaks
eng
uncontrolled
self-sustained activity
eng
uncontrolled
cortical oscillations
eng
uncontrolled
irregular firing activity
eng
uncontrolled
hierarchical modular networks
eng
uncontrolled
cortical network models
eng
uncontrolled
intrinsic neuronal diversity
eng
uncontrolled
up-down states
eng
uncontrolled
chaotic neural dynamics
Institut für Physik und Astronomie
Referiert
Import