An efficient method using Bayesian and linear classifiers is presented for analyzing the dynamics of information in high dimensional circuit states, and applied to investigate emergent computation in generic cortical microcircuit models. It is shown that such recurrent circuits of spiking neurons have an inherent capability to carry out rapid computations on complex spike patterns, merging information contained in the order of spike arrival with previously acquired context information.
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