COMPUTATIONAL NEUROBIOLOGY GRADUATE PROGRAM
terry@salk.edu
terry at salk.edu
Wed Dec 8 23:39:36 EST 1999
DEADLINE: JANUARY 7, 2000
COMPUTATIONAL NEUROBIOLOGY GRADUATE PROGRAM
Department of Biology -- University of California, San Diego
http://www.biology.ucsd.edu/compneuro/
The goal of the Computational Neurobiology Graduate Program at UCSD is
to train a new generation of researchers who are equally at home measuring
large-scale brain activity, analyzing the data with advanced computational
techniques, and developing new models for brain development and function.
Candidates from a wide range of backgrounds are invited to apply,
including Biology, Psychology, Computer Science, Physics and Mathematics.
The three major themes in the training program are:
1. Neurobiology of Neural Systems -- Anatomy, physiology and behavior of
systems of neurons. Using modern neuroanatomical, neuropharmacological
and electrophysiological techniques. Lectures, wet laboratories and computer
simulations, as well as research rotations. Major new imaging and recording
techniques also will be taught, including two-photon laser scanning microscopy
and functional magnetic resonance imaging (fMRI).
2. Algorithms and Realizations for the Analysis of Neuronal Data --
New algorithms and techniques for analyzing data obtained from physiological
recording, with an emphasis on recordings from large populations of neurons
with imaging and multielectrode recording techniques. New methods for the
study of co-ordinated activity, such as multi-taper spectral analysis and
Independent Component Analysis (ICA).
3. Neuroinformatics, Dynamics and Control of Systems of Neurons --
Theoretical aspects of single cell function and emergent properties
as many neurons interact among themselves and react to sensory inputs.
A synthesis of approaches from mathematics and physical sciences
as well as biology will be used to explore the collective properties
and nonlinear dynamics of neuronal systems, as well as issues of sensory
coding and motor control.
Requests for application materials should be sent to the Graduate
Admissions Office, Department of Biology 0348, 9500 Gilman Drive,
UCSD, La Jolla, CA, 92093-0348: [gradprog at biology.ucsd.edu].
The deadline for completed application materials, including letters of
reference, is JANUARY 7, 2000.
More information about applying to the UCSD Biology Graduate Program:
http://www-biology.ucsd.edu/sa/Admissions.html
The Biology Department home page is located at:
http://www-biology.ucsd.edu/
Other inquiries about the Computational Neurobiology Graduate
Program should be directed to:
Terrence Sejnowski
Institute for Neural Computation 0523
University of California, San Diego
La Jolla, CA 92093
tsejnowski at ucsd.edu
Participating Faculty include:
* Henry Abarbanel (Physics): Nonlinear and oscillatory dynamics;
modeling central pattern generators in the lobster stomatogastric ganglion.
Director of the Institute for Nonlinear Systems at UCSD.
* Thomas Albright (Salk Institute): Motion processing in primate visual cortex;
linking single neurons to perception; fMRI in awake, behaving monkeys.
Director, Sloan Center for Theoretical Neurobiology.
* Darwin Berg (Biology): Regulation synaptic components, assembly and
localization, function and long-term stability. Former Chairman of Biology.
* Garrison Cottrell (Computer Science and Engineering): Dynamical neural
network models and learning algorithms.
* Mark Ellisman (Neurosciences, School of Medicine): High resolution electron
and light microscopy; anatomical reconstructions. Director, National Center for
Microscopy and Imaging Research.
* Robert Hecht-Nielsen (Electrical and Computer Engineering): Neural computation
and the functional organization of the cerebral cortex. Founder of Hecht-Nielsen
Corporation.
* Harvey Karten (Neurosciences, School of Medicine): Anatomical, physiological
and computational studies of the retina and optic tectum of birds and squirrels.
* David Kleinfeld (Physics):Active sensation in rat somatosensation; properties
of neuronal assemblies; optical imaging of large-scale activity. Co-director,
Analysis of Neural Data Workshop (MBL).
* William Kristan (Biology): Neuroethology of leech; functional and developmental
studies of the leech nervous system, including computational studies of the
bending reflex and locomotion. Director of the Neurosciences Graduate Program.
* Herbert Levine (Physics): Nonlinear dynamics and pattern formation in physical
and biological systems, including cardiac dynamics and the growth and form of
bacterial colonies.
* Javier Movellan (Cognitive Science): Sensory fusion and learning algorithms
for continuous stochastic systems.
* Mu-ming Poo (Biology): Mechanisms for synaptic plasticity; developmental
plasticity and learning in nervous systems; development of sensory maps in
lower vertebrate visual systems.
* Mikhael Rabinovich (Institute for Nonlinear Science): Dynamical systems
analysis of the stomatogastric ganglion of the lobster and the antenna lobe
of insects.
* Terrence Sejnowski (Salk Institute/Biology): Computational neurobiology;
physiological studies of neuronal reliability and synaptic mechanisms.
Director, Institute for Neural Computation.
* Martin Sereno (Cognitive Science): Neural bases of visual cognition and
language using anatomical, electrophysiological, computational, and non-invasive
brain imaging techniques.
* Nicholas Spitzer (Biology): Regulation of ionic channels and neurotransmitters
in neurons; effects of electrical activity in developing neurons on neural function.
Chair of the Neurobiology Section.
* Charles Stevens (Salk Institute): Synaptic physiology; physiological studies
and biophysical models of synaptic plasticity in hippocampal neurons.
* Roger Tsien (Chemistry): Second messenger systems in neurons; development of
new optical and MRI probes of neuron function, including calcium indicators
and caged neurotransmitters.
* Mark Whitehead (Neurosurgery, School of Medicine): Peripheral and central
taste systems; anatomical and functional studies of regions in the caudal
brainstem important for feeding behavior.
* Ruth Williams (Mathematics): Probabilistic analysis of stochastic systems
and continuous learning algorithms.
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