A framework for integrating the songbird brain.
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Biological systems by default involve complex components with complex relationships. To decipher how biological systems work, we assume that one needs to integrate information over multiple levels of complexity. The songbird vocal communication system is ideal for such integration due to many years of ethological investigation and a discreet dedicated brain network. Here we announce the beginnings of a songbird brain integrative project that involves high-throughput, molecular, anatomical, electrophysiological and behavioral levels of analysis. We first formed a rationale for inclusion of specific biological levels of analysis, then developed high-throughput molecular technologies on songbird brains, developed technologies for combined analysis of electrophysiological activity and gene regulation in awake behaving animals, and developed bioinformatic tools that predict causal interactions within and between biological levels of organization. This integrative brain project is fitting for the interdisciplinary approaches taken in the current songbird issue of the Journal of Comparative Physiology A and is expected to be conducive to deciphering how brains generate and perceive complex behaviors.
Gene Expression Profiling
Gene Expression Regulation, Developmental
Neural Networks (Computer)
Published Version (Please cite this version)10.1007/s00359-002-0358-y
Publication InfoJarvis, ED; Smith, VA; Wada, K; Rivas, MV; McElroy, M; Smulders, TV; ... Lin, S (2002). A framework for integrating the songbird brain. J Comp Physiol A Neuroethol Sens Neural Behav Physiol, 188(11-12). pp. 961-980. 10.1007/s00359-002-0358-y. Retrieved from https://hdl.handle.net/10161/11222.
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Associate Professor of Molecular Genetics and Microbiology
My laboratory is interested in fungal genomics.In particular we use genomic sequencing of fungal strains and species in comparative analysis. Starting with the sequencing of Saccharomyces cerevisiae strain S288C, I have been involved in the genome sequencing and annotation of Ashbya gossypii, Cryptococcus neoformans var. grubii and ~100 additional S. cerevisiae strains. We currently use Illumina paired end and mate paired sequencin
Professor in the Department 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
Adjunct Professor in the Dept. 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
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