Computational inference of neural information flow networks.
dc.contributor.author | Smith, V Anne | |
dc.contributor.author | Yu, Jing | |
dc.contributor.author | Smulders, Tom V | |
dc.contributor.author | Hartemink, Alexander J | |
dc.contributor.author | Jarvis, Erich D | |
dc.coverage.spatial | United States | |
dc.date.accessioned | 2014-12-15T16:50:19Z | |
dc.date.issued | 2006-11-24 | |
dc.description.abstract | 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. | |
dc.identifier | ||
dc.identifier | 06-PLCB-RA-0375R2 | |
dc.identifier.eissn | 1553-7358 | |
dc.identifier.uri | ||
dc.language | eng | |
dc.publisher | Public Library of Science (PLoS) | |
dc.relation.ispartof | PLoS Comput Biol | |
dc.relation.isversionof | 10.1371/journal.pcbi.0020161 | |
dc.subject | Action Potentials | |
dc.subject | Animals | |
dc.subject | Auditory Cortex | |
dc.subject | Auditory Perception | |
dc.subject | Computer Simulation | |
dc.subject | Electroencephalography | |
dc.subject | Evoked Potentials, Auditory | |
dc.subject | Finches | |
dc.subject | Information Theory | |
dc.subject | Models, Neurological | |
dc.subject | Nerve Net | |
dc.subject | Synaptic Transmission | |
dc.title | Computational inference of neural information flow networks. | |
dc.type | Journal article | |
duke.contributor.orcid | Hartemink, Alexander J|0000-0002-1292-2606 | |
pubs.author-url | ||
pubs.begin-page | e161 | |
pubs.issue | 11 | |
pubs.organisational-group | Basic Science Departments | |
pubs.organisational-group | Computer Science | |
pubs.organisational-group | Duke | |
pubs.organisational-group | Duke Institute for Brain Sciences | |
pubs.organisational-group | Institutes and Provost's Academic Units | |
pubs.organisational-group | Neurobiology | |
pubs.organisational-group | School of Medicine | |
pubs.organisational-group | Trinity College of Arts & Sciences | |
pubs.organisational-group | University Institutes and Centers | |
pubs.publication-status | Published | |
pubs.volume | 2 |
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