Computational inference of neural information flow networks.

Computational inference of neural information flow networks. Research Abstract Details 

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Computational inference of neural information flow networks. Abstract Text:

V Anne Smith,Jing Yu,Tom V Smulders,Alexander J Hartemink,Erich D Jarvis,

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.

Computational inference of neural information flow networks. Publishing Authors By Initials

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

For similar synaptic transmission research abstracts see: synaptic transmission research

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Computational inference of neural information flow networks. Journal Published:

PUBLICATION TYPE: Research Support, U.S. Gov't,

Journal: PLoS computational biology

VOLUME: 2

Page Numbers: e161

Journal Abbreviation: PLoS Comput. Biol.

ISSN: 1553-7358

DAY: 12

MONTH: 10

YEAR: 2006

Computational inference of neural information flow networks. Information

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LANGUAGE: eng

NlmUniqueID: 101238922

Computational inference of neural information flow networks. Keywords Mesh Terms:

KEYWORDS: Synaptic Transmission

MESH TERMS: physiology

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Grant and Affiliation Information for Computational inference of neural information flow networks.

AFFILIATION: Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, United States of America.

Country: United States

AGENCY: United States NIDCD

GRANT: R01 DC7996

ACRONYM: DC

MEDLINETA: PLoS Comput Biol

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