article

The high-conductance state of cortical networks

Authors:

Stefan RotterAuthors Info & Claims

Neural Computation, Volume 20, Issue 1

Pages 1 - 43

https://doi.org/10.1162/neco.2008.20.1.1

Published: 01 January 2008 Publication History

Abstract

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.

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cover image Neural Computation

Neural Computation Volume 20, Issue 1

January 2008

309 pages

ISSN:0899-7667

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MIT Press

Cambridge, MA, United States

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Published: 01 January 2008

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Thieme ABelgrave DDoherty G(2020)Machine Learning in Mental HealthACM Transactions on Computer-Human Interaction10.1145/339806927:5(1-53)Online publication date: 17-Aug-2020
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