Identification of functional connections in biological neural networks using dynamical Bayesian networks
Dong, Chaoyi and Yue, Hong (2016) Identification of functional connections in biological neural networks using dynamical Bayesian networks. IFAC-PapersOnLine, 49 (26). pp. 178-183. ISSN 1474-6670 (https://doi.org/10.1016/j.ifacol.2016.12.122)
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Abstract
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.
ORCID iDs
Dong, Chaoyi and Yue, Hong
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Item type: Article ID code: 57826 Dates: DateEvent9 October 2016Published24 July 2016AcceptedKeywords: pulsed neural networks, dynamic Bayesian networks, minimum description length, casual connection, synthetic biological networks, network structure identification, Electrical Engineering. Electronics Nuclear Engineering, Electrical and Electronic Engineering Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 16 Sep 2016 13:28 Last modified: 08 Aug 2023 08:41 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/57826