A framework for integrating the songbird brain.
Abstract
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.
Type
Journal articleSubject
AnimalsAuditory Pathways
Bayes Theorem
Brain
Brain Mapping
Computational Biology
Computer Simulation
DNA-Binding Proteins
Electrophysiology
Gene Expression Profiling
Gene Expression Regulation, Developmental
Gene Library
Learning
Models, Neurological
Motor Activity
Nerve Net
Neural Networks (Computer)
Songbirds
Transcription Factors
Vocalization, Animal
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https://hdl.handle.net/10161/11222Published Version (Please cite this version)
10.1007/s00359-002-0358-yPublication Info
(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.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Fred Samuel Dietrich
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
Alexander J. Hartemink
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
Erich David Jarvis
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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