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

In this study, we applied Bayesian decision theory to evaluate the informat ion contained in neural spike trains. We used the spike statistics from 90% of the labelled trials to classify each of the remaining unlabelled trials . Classification rate were computed at different post-stimulus time within time windows of different durations. This allowed us to visualize and evalu ate the information content of the spike trains in a scale-space representa tion. We found that discrimination of patterns within the receptive fields 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 glob al contextual information can be accomplished at a later time. (C) 2000 Els evier Science B.V. All rights reserved.