Abstract:Abstract: Investigations of the underlying structural characteristics and network properties of biological networks are crucial to understanding the system-level regulatory mechanism of network behaviors. Here, a dynamical Bayesian network structure searching method (DBNSSM) was investigated based on the minimum de-scription length (MDL) criteria to identify and locate functional connections among pulsed neural networks (PNNs), which are typical in synthetic biological neural networks (NNs). A score evaluated for each candidate network structure includes two factors: 1) the complexity of the network, and 2) the likelihood of the network structure based on network dynamical response data. These two factors are combined together in selection of network structure. The DBNSSM was then used to analyze the time-series data from the PNNs, thereby discerning causal connections which collectively show the network structures. Numerical studies illustrated the effectiveness of the proposed strategy in structure identification of synthetic biological networks.
引用本文:
陈晓艳, 董朝轶. 动态贝叶斯网络结构搜索法辨识生物神经网络连接[J]. 生命科学研究, 2017, 21(6): 527-533.
CHEN Xiao-yan, DONG Chao-yi. Identification of Biological Neural Network Connections by Dynamical Bayesian Network Structure Searching. Life Science Research, 2017, 21(6): 527-533.