title: Synaptic unreliability facilitates information transmission in balanced cortical populations.
abstract: Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level.
authors.affiliation: Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany.|Bernstein Center for Computational Neuroscience, Tübingen, Germany.
authors.forename: Leon A
authors.initials: LA
authors.lastname: Gatys
authors.affiliation: Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany.|Bernstein Center for Computational Neuroscience, Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, Tübingen, Germany.|Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.
authors.forename: Alexander S
authors.initials: AS
authors.lastname: Ecker
authors.affiliation: Max Planck Institute for Brain Research, Frankfurt, Germany.
authors.forename: Tatjana
authors.initials: T
authors.lastname: Tchumatchenko
authors.affiliation: Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany.|Bernstein Center for Computational Neuroscience, Tübingen, Germany.|Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Bethge
mesh_terms: D001921:Brain / Q000166:cytology*; D008959:Models, Neurological; D009474:Neurons / Q000166:cytology; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission