Identification of functional connections in biological neural networks using dynamical Bayesian networks

Chaoyi Dong, Hong Yue

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
107 Downloads (Pure)


Investigation of the underlying structural characteristics and network properties of biological networks is crucial to understanding the system-level regulatory mechanism of network behaviors. A Dynamic Bayesian Network (DBN) identification method is developed based on the Minimum Description Length (MDL) to identify and locate functional connections among Pulsed Neural Networks (PNN), which are typical in synthetic biological networks. A score of MDL is evaluated for each candidate network structure which includes two factors: i) the complexity of the network; and ii) the likelihood of the network structure based on network dynamic response data. These two factors are combined together to determine the network structure. The DBN is then used to analyze the time-series data from the PNNs, thereby discerning causal connections which collectively show the network structures. Numerical studies on PNN with different number of nodes illustrate the effectiveness of the proposed strategy in network structure identification.
Original languageEnglish
Pages (from-to)178-183
Number of pages6
Issue number26
Publication statusPublished - 9 Oct 2016
EventThe 6th IFAC Conference on Foundations of Systems Biology in Engineering - Otto-von-Guericke University , Magdeburg, Germany
Duration: 9 Oct 201612 Oct 2016


  • pulsed neural networks
  • dynamic Bayesian networks
  • minimum description length
  • casual connection
  • synthetic biological networks
  • network structure identification


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