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.
Klinkner, K.L., Shalizi, C.R., Camperi, M.F. Measuring shared information and coordinated activity in neuronal networks (2005) Advances in Neural Information Processing Systems, pp. 667-674.