It is important to explore potential structural characteristics of biological networks and regulatory mechanisms of network behaviors at the system level. In this study, a dynamic Bayesian network structure search method (DBNSSM) based on a genetic algorithm is employed to infer and locate functional connections in pulsed neural networks (PNNs) as typical artificial neural networks. In the process of network structure searching, a minimum description length score is calculated for each candidate network structure. The score indicates two characteristics of the network structure: (1) the likelihood based on network dynamic response data and (2) the complexity. Both should be considered together on selecting network structures. The DBNSSM is applied to analyze time-series data from PNNs, thereby discerns functional connections showing network structures collectively. It is feasible to analyze multichannel electrophysiological data of biological neural networks using the DBNSSM.
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