Correlation transfer in neuronal populations

Abstract

Event Abstract Back to Event Correlation transfer in neuronal populations Correlated activity in neural tissue can significantly impact the information carried by a population of neurons. However, there are relatively few analytical results that provide a mechanistic understanding of how correlations are generated and propagated. We start by examining this question using the integrate and fire model which has inspired many developments in theoretical neuroscience. In the second part of the presentation we demonstrate these results using new numerical methods for the simulation of networks of stochastic integrate and fire neurons. These methods are several orders of magnitude faster than typical Monte Carlo simulations. Outputs of a population of neurons are typically pooled to form the input to cells downstream. We provide simple analytical results that describe this situation and show that correlations can be propagated in a counterintuitive manner: Small correlations in the population can translate into large input correlations after such pooling. On the other hand, an increase of correlations within a populations can decrease correlations between populations. It has recently been observed that the transfer of correlations from input currents to output spike trains depends on the firing rate in neuron models and experiments in vitro. Over rapid time scales, correlation transfer increases with both spike time variability and rate; the dependence on variability disappears at large time scales. We show that the behavior of the perfect integrate and fire (PIF) model is quite different: correlations are transferred perfectly over large windows. We give a full description of correlation transfer in PIFs, and provide an intuitive understanding of how cross-correlograms are transformed in networks of such neurons. Although the PIF preserves correlations over long time windows, it tends to "smear out" the cross-correlogram, due to thresholding.This analysis shows that the decorrelation typically observed in a feedforward configuration of more realistic neurons results both from the existence of a firing threshold and "memory loss" induced by the leak. Therefore, the peculiarity of the PIF model can be used to provide an intuitive understanding of the behavior of more complex models. To examine when these results apply to more complex models requires numerical simulations. We present a fast and accurate finite volume method to approximate the solution of the Fokker-Planck equation that models the multivariate density of the subthreshold voltages of stochastic integrate and fire neurons. The discretization of the boundary conditions offers a particular challenge, as standard operator splitting approaches cannot be applied without modification. In comparison to Monte Carlo methods, the present approach offers improved accuracy, and decreases computation times by several orders of magnitude. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Correlation transfer in neuronal populations. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.311 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Feb 2009; Published Online: 04 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.