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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Control and analysis of spike trains' correlations

Michael Krumin, Avner Shimron and Shy Shoham*

Author Affiliations

Faculty of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, 32000, Israel

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BMC Neuroscience 2009, 10(Suppl 1):P183  doi:10.1186/1471-2202-10-S1-P183

The electronic version of this article is the complete one and can be found online at:

Published:13 July 2009

© 2009 Krumin et al; licensee BioMed Central Ltd.

Poster presentation

Emerging evidence indicates that information processing as well as learning and memory processes in both the network and single-neuron levels are highly dependent on the correlation structure of multiple spike trains. Contemporary experimental as well as theoretical studies that involve quasi-realistic neuronal stimulation thus require a method for controlling spike-train correlations. We introduce a general new strategy for generating multiple spike trains with exactly controlled mean firing rates and correlation structure (defined in terms of auto- and cross-correlation functions) [1]. Our approach non-linearly transforms random Gaussian-distributed processes with a pre-distorted correlation structure into non-negative rate processes, which are then used to generate doubly stochastic Poisson point processes with the required correlation structure. We show how this approach can be used to generate stationary or non-stationary processes, and conversely for the identification of Linear-Nonlinear-Poisson (LNP) encoding models purely from a given system's input and output correlation structures. Correlation-based identification is a "blind" alternative to reverse correlation and related techniques.


This work was supported by Israeli Science Foundation grant #1248/06 and European Research Council starting grant #211055.


  1. Krumin M, Shoham S: Generation of spike trains with controlled auto- and cross-correlation functions.

    Neural Comput 2009, in press. PubMed Abstract | Publisher Full Text OpenURL