Skip to main content

Computational inference of neural information flow networks.

Publication ,  Journal Article
Smith, VA; Yu, J; Smulders, TV; Hartemink, AJ; Jarvis, ED
Published in: PLoS Comput Biol
November 24, 2006

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

November 24, 2006

Volume

2

Issue

11

Start / End Page

e161

Location

United States

Related Subject Headings

  • Synaptic Transmission
  • Nerve Net
  • Models, Neurological
  • Information Theory
  • Finches
  • Evoked Potentials, Auditory
  • Electroencephalography
  • Computer Simulation
  • Bioinformatics
  • Auditory Perception
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Smith, V. A., Yu, J., Smulders, T. V., Hartemink, A. J., & Jarvis, E. D. (2006). Computational inference of neural information flow networks. PLoS Comput Biol, 2(11), e161. https://doi.org/10.1371/journal.pcbi.0020161
Smith, V Anne, Jing Yu, Tom V. Smulders, Alexander J. Hartemink, and Erich D. Jarvis. “Computational inference of neural information flow networks.PLoS Comput Biol 2, no. 11 (November 24, 2006): e161. https://doi.org/10.1371/journal.pcbi.0020161.
Smith VA, Yu J, Smulders TV, Hartemink AJ, Jarvis ED. Computational inference of neural information flow networks. PLoS Comput Biol. 2006 Nov 24;2(11):e161.
Smith, V. Anne, et al. “Computational inference of neural information flow networks.PLoS Comput Biol, vol. 2, no. 11, Nov. 2006, p. e161. Pubmed, doi:10.1371/journal.pcbi.0020161.
Smith VA, Yu J, Smulders TV, Hartemink AJ, Jarvis ED. Computational inference of neural information flow networks. PLoS Comput Biol. 2006 Nov 24;2(11):e161.

Published In

PLoS Comput Biol

DOI

EISSN

1553-7358

Publication Date

November 24, 2006

Volume

2

Issue

11

Start / End Page

e161

Location

United States

Related Subject Headings

  • Synaptic Transmission
  • Nerve Net
  • Models, Neurological
  • Information Theory
  • Finches
  • Evoked Potentials, Auditory
  • Electroencephalography
  • Computer Simulation
  • Bioinformatics
  • Auditory Perception