title: Noise suppression and surplus synchrony by coincidence detection.
abstract: The functional significance of correlations between action potentials of neurons is still a matter of vivid debate. In particular, it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high input correlation, in the presence of synchrony, a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks.
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany. schultze-kraft@tu-berlin.de
authors.forename: Matthias
authors.initials: M
authors.lastname: Schultze-Kraft
authors.forename: Markus
authors.initials: M
authors.lastname: Diesmann
authors.forename: Sonja
authors.initials: S
authors.lastname: Grün
authors.forename: Moritz
authors.initials: M
authors.lastname: Helias
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004058:Diffusion; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission