Frontiers in Computational Neuroscience (Mar 2016)
Mechanisms of self-sustained oscillatory states in hierarchical modular networks with mixtures of electrophysiological cell types
Abstract
In a network with a mixture of different electrophysiologicaltypes 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 interms of individual dynamics of the neurons. In particular, weobserve that the crucial role both in interruption and in resumptionof global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is moreinfluenced by their presynaptic environment in the networkthan by their formal types,assigned in accordance with their response to constant current.
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