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The High-Conductance State of Cortical Networks

arvind_kumar@brown.edu Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, D-79104 Freiburg, Germany, and Bernstein Center for Computational Neuroscience, D-79104 Freiburg, Germany

Sven Schrader

schrader@biologie.uni-freiburg.de Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, D-79104 Freiburg, Germany, and Bernstein Center for Computational Neuroscience, D-79104 Freiburg, Germany

Ad Aertsen

aertsen@biologie.uni-freiburg.de Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, D-79104 Freiburg, Germany, and Bernstein Center for Computational Neuroscience, D-79104 Freiburg, Germany

Stefan Rotter

stefan.rotter@biologie.uni-freiburg.de Theory and Data Analysis, Institute for Frontier Areas of Psychology and Mental Health, D-79098 Freiburg, Germany, and Bernstein Center for Computational Neuroscience, D-79104 Freiburg, Germany

We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.