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Computational Inference of Neural Information Flow Networks
2005
PLoS Computational Biology
Determining how information flows along anatomical brain pathways is a fundamental requirement for understanding how animals perceive their environments, learn, and behave. Attempts to reveal such neural information flow have been made using linear computational methods, but neural interactions are known to be nonlinear. Here, we demonstrate that a dynamic Bayesian network (DBN) inference algorithm we originally developed to infer nonlinear transcriptional regulatory networks from gene
doi:10.1371/journal.pcbi.0020161.eor
fatcat:2ydvpcybizdxdctdtuji6efuv4