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Dependence of Neuronal Correlations on Filter Characteristics and Marginal Spike Train Statistics

tom.tetzlaff{at}umb.no Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, D-79104 Freiburg, Germany, and Institute of Mathematical Sciences and Technology, Norwegian University of Life Sciences, N-1432 Ås, Norway

Stefan Rotter

stefan.rotter{at}biologie.uni-freiburg.de Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, D-79104 Freiburg, Germany, and Theory and Data Analysis, Institute for Frontier Areas of Psychology and Mental Health, D-79098 Freiburg, Germany

Eran Stark

eranst{at}ekmd.huji.ac.il Department of Physiology, Hebrew University of Jerusalem, Jerusalem 91120, Israel

Moshe Abeles

abelesm{at}mail.biu.asc.il Gonda Brain Research Institute, Bar Ilan University, Ramat-Gan 52900, Israel

Ad Aertsen

aertsen{at}biologie.uni-freiburg.de Bernstein Center for Computational Neuroscience, and Neurobiology and Biophysics, Faculty of Biology, Albert-Ludwigs-University, D-79104 Freiburg, Germany

Markus Diesmann

diesmann{at}brain.riken.jp Bernstein Center for Computational Neuroscience, Albert-Ludwigs-University, D-79104 Freiburg, Germany, and Brain Science Institute, RIKEN, Wako City, Saitama 351-0198, Japan

Correlated neural activity has been observed at various signal levels (e.g., spike count, membrane potential, local field potential, EEG, fMRI BOLD). Most of these signals can be considered as superpositions of spike trains filtered by components of the neural system (synapses, membranes) and the measurement process. It is largely unknown how the spike train correlation structure is altered by this filtering and what the consequences for the dynamics of the system and for the interpretation of measured correlations are. In this study, we focus on linearly filtered spike trains and particularly consider correlations caused by overlapping presynaptic neuron populations. We demonstrate that correlation functions and statistical second-order measures like the variance, the covariance, and the correlation coefficient generally exhibit a complex dependence on the filter properties and the statistics of the presynaptic spike trains. We point out that both contributions can play a significant role in modulating the interaction strength between neurons or neuron populations. In many applications, the coherence allows a filter-independent quantification of correlated activity. In different network models, we discuss the estimation of network connectivity from the high-frequency coherence of simultaneous intracellular recordings of pairs of neurons.

This article has been cited by other articles:

				B. Kriener, T. Tetzlaff, A. Aertsen, M. Diesmann, and S. Rotter Correlations and Population Dynamics in Cortical Networks Neural Comput., September 1, 2008; 20(9): 2185 - 2226. [Abstract] [Full Text] [PDF]