Stochastic interaction in associative nets

Authors:
Thomas Wennekers;Nihat Ay
Affiliations:
Centre for Theoretical and Computational Neuroscience, Plymouth Institute of Neuroscience, University of Plymouth, Portland Square, Room 218, Drake Circus, Plymouth PL4 8AA, United Kingdom;Institute of Mathematics, University of Erlangen, 91054 Erlangen, Germany
Venue:
Neurocomputing
Year:
2005

Citing 4
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Abstract

Spatio-temporal correlations in spike trains of simultaneously recorded neurons characterize stochastic interactions in neural networks. Information theoretic measures for ''spatial'' and ''temporal stochastic interaction'' can measure the total amount of dependence in a set of cells. In the present work we calculate these interaction measures for associative networks, the most prominent models for cortical gamma-oscillations and precisely repetiting spike patterns (synfire chains). Stochastic interaction in these networks appears to be very high conflicting with the common belief that neurons are largely independent Poisson processes.