Functional
consequences of correlated excitatory and inhibitory conductances in
cortical networks
Jens Kremkow1, 2, 3  ,
Laurent U. Perrinet1, Guillaume S. Masson1
and Ad Aertsen2, 3
| (1) |
Institut de Neurosciences
Cognitives de la Méditerranée, UMR6193 CNRS—Aix-Marseille Université, 31
chemin Joseph Aiguier, 13402 Marseille Cedex 20, France |
| (2) |
Neurobiology and
Biophysics, Faculty of Biology, Albert-Ludwig University,
Schänzlestrasse 1, 79104 Freiburg, Germany |
| (3) |
Bernstein Center for
Computational Neuroscience, Hansastrasse 9A, 79104 Freiburg,
Germany |
Received: 19 March 2009 Revised:
8 April 2010 Accepted: 20 April 2010 Published
online: 19 May 2010
Action Editor: X.-J. Wang
Abstract Neurons
in the neocortex receive a large number of excitatory and inhibitory
synaptic inputs. Excitation and inhibition dynamically
balance each other, with inhibition lagging excitation by
only few milliseconds. To characterize the functional consequences
of such correlated excitation and inhibition, we studied
models in which this correlation structure is induced by feedforward
inhibition (FFI). Simple circuits show that an effective FFI
changes the integrative behavior of neurons such that only synchronous
inputs can elicit spikes, causing the responses to be sparse
and precise. Further, effective FFI increases the selectivity
for propagation of synchrony through a feedforward network,
thereby increasing the stability to background activity. Last,
we show that recurrent random networks with effective
inhibition are more likely to exhibit dynamical network activity states
as have been observed in vivo. Thus, when a
feedforward signal path is embedded in such recurrent network, the
stabilizing effect of effective inhibition
creates an suitable substrate for signal propagation. In
conclusion, correlated excitation and inhibition support the notion
that synchronous spiking may be important for cortical
processing.
Keywords Correlated
conductances - Synaptic integration - Sparse
coding - Signal propagation
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