A Bayesian decision approach to evaluate local and contextual information in spike trains

Abstract In this study, we applied Bayesian decision theory to evaluate the information contained in neural spike trains. We used the spike statistics from 90% of the labelled trials to classify each of the remaining unlabelled trials. Classi"cation rate were computed at di!erent post-stimulus time within time windows of di!erent durations. This allowed us to visualize and evaluate the information content of the spike trains in a scale-space representation. We found that discrimination of patterns within the receptive "elds of the neurons can be accomplished at an early stage of the response within a relatively small time window (5}30 ms), while the discrimination of global contextual information can be accomplished at a later time