Picture water droplets

Developing mathematical theories of the physical world: Open Access research on fluid dynamics from Strathclyde

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Mathematics & Statistics, where continuum mechanics and industrial mathematics is a specialism. Such research seeks to understand fluid dynamics, among many other related areas such as liquid crystals and droplet evaporation.

The Department of Mathematics & Statistics also demonstrates expertise in population modelling & epidemiology, stochastic analysis, applied analysis and scientific computing. Access world leading mathematical and statistical Open Access research!

Explore all Strathclyde Open Access research...

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. p. 1. ISSN 1474-6670

Text (Dong-Yue-IFACO2016-Identification-of-functional-connections-in-biological-neural-networks)
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo

Download (340kB) | Preview


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.