Bayesian Network Inference to Estimate the Functional Connectivity of Cultured Neuronal Networks

Abstract

Microelectrode array recordings from single neurons generate multidimensional data (spike trains) that contains vast amount of information on underlying neural dynamics. Typically, the data analysis procedure is very time consuming, which greatly hinders the experimental throughputs. Bioinformatics community also deals with high dimensional data sets and the underlying mathematics of data analysis used in this field is very similar to that used in neural informatics. Here, we attempt to use the well-established data analysis procedure (Bayesian network inference) in Bioinformatics and utilized it to estimate the functional connectivity of cultured neural networks based on multichannel spike trains. The basic analysis procedure could be easily extended to in vivo neural spike data analysis for various neural engineering applications