Computational inference of neural information flow networks.

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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.





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Smith, V Anne, Jing Yu, Tom V Smulders, Alexander J Hartemink and Erich D Jarvis (2006). Computational inference of neural information flow networks. PLoS Comput Biol, 2(11). p. e161. 10.1371/journal.pcbi.0020161 Retrieved from

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Alexander J. Hartemink

Professor of Computer Science

Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection


Erich David Jarvis

Adjunct Professor in the Deptartment of Neurobiology

Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. They use an integrative approach that combines behavioral, anatomical, electrophysiological and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The generality of the discoveries is tested in other vocal learning orders, such as parrots and hummingbirds, as well as non-vocal learners, such as pigeons and non-human primates. Some of the questions require performing behavior/molecular biology experiments in freely ranging animals, such as hummingbirds in tropical forest of Brazil. Recent results show that in songbirds, parrots and hummingbirds, perception and production of song are accompanied by anatomically distinct patterns of gene expression. All three groups were found to exhibit vocally-activated gene expression in exactly 7 forebrain nuclei that are very similar to each other. These structures for vocal learning and production are thought to have evolved independently within the past 70 million years, since they are absent from interrelated non-vocal learning orders. One structure, Area X of the basal ganglia's striatum in songbirds, shows large differential gene activation depending on the social context in which the bird sings. These differences may reflect a semantic content of song, perhaps similar to human language.

The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function. These goals are further achieved by combined collaborative efforts with the laboratories of Drs. Mooney and Nowicki at Duke University, who study respectively behavior and electrophysiological aspects of songbird vocal communication.

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