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LEE LAB

for Biological and Machine Intelligence Research

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

Elise Cassidente, Xiaogang Yan, Tai Sing Lee | Neurocomputing | 2000 | 10.1016/S0925-2312(00)00273-3 | PDF
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. Classification rate were computed at different post-stimulus time within time windows of different 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 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 global contextual information can be accomplished at a later time.