Computational inference of neural information flow networks.

Journal Article (Journal Article)

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 expression data collected with microarrays is also successful at inferring nonlinear neural information flow networks from electrophysiology data collected with microelectrode arrays. The inferred networks we recover from the songbird auditory pathway are correctly restricted to a subset of known anatomical paths, are consistent with timing of the system, and reveal both the importance of reciprocal feedback in auditory processing and greater information flow to higher-order auditory areas when birds hear natural as opposed to synthetic sounds. A linear method applied to the same data incorrectly produces networks with information flow to non-neural tissue and over paths known not to exist. To our knowledge, this study represents the first biologically validated demonstration of an algorithm to successfully infer neural information flow networks.

Full Text

Duke Authors

Cited Authors

  • Smith, VA; Yu, J; Smulders, TV; Hartemink, AJ; Jarvis, ED

Published Date

  • November 24, 2006

Published In

Volume / Issue

  • 2 / 11

Start / End Page

  • e161 -

PubMed ID

  • 17121460

Pubmed Central ID

  • PMC1664702

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.0020161


  • eng

Conference Location

  • United States