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Conference Proceeding

Publication Date



Most nervous systems encode information about stimuli in the respond- ing activity of large neuronal networks. This activity often manifests itself as dynamically coordinated sequences of action potentials. Since multiple electrode recordings are now a standard tool in neuroscience research, it is important to have a measure of such network-wide behav- ioral coordination and information sharing, applicable to multiple neural spike train data. We propose a new statistic, informational coherence , which measures how much better one unit can be predicted by knowing the dynamical state of another. We argue informational coherence is a measure of association and shared information which is superior to tradi- tional pairwise measures of synchronization and correlation. To find the dynamical states, we use a recently-introduced algorithm which recon- structs effective state spaces from stochastic time series. We then extend the pairwise measure to a multivariate analysis of the network by estimat- ing the network multi-information. We illustrate our method by testing it on a detailed model of the transition from gamma to beta rhythms.


Copyright 2005 MIT Press.

Journal home page: Advances in Neural Information Processing Systems

Available at NIPS Proceedings.

Presented at the Nineteenth Annual Conference on Neural Information Processing Systems, Vancouver Canada, December 2005.