[Comp-neuro] Computational Neurobiology Graduate Program at UCSD

Terry Sejnowski terry at salk.edu
Mon Jul 7 00:30:46 CEST 2008


                   DEADLINE: DECEMBER 15, 2008

            COMPUTATIONAL NEUROBIOLOGY SPECIALIZATION
Neurosciences Graduate Training Program - University of California, San Diego
           http://neurograd.ucsd.edu/doctoral/cnspec.html

Overview

The Computational Neurobiology Specialization is a new facet of the 
broader Neuroscience Graduate Program at UCSD.  The goal of the 
specialization is to train the next generation of neuroscientists with 
the broad range of computational and analytical skills that are 
essential to understand the organization and function of complex 
neural systems.  The specialization is intended for students with 
backgrounds in neuroscience, physics, chemistry, biology, psychology, 
computer science, engineering, and mathematics.

The specialization allows Neuroscience students to concentrate on a 
focused program of rigorous course work in both the theoretical and 
experimental aspects of computational neuroscience.  Students are 
encouraged to pursue thesis research that includes both an 
experimental and a computational component, often arranged by the 
student as a collaboration between two research groups.  Upon 
achievement of degree requirements, students will receive a diploma 
indicating both their successful completion of the broader 
Neuroscience Program as well as their specialization in Computational 
Neurobiology.

Themes

The program is focused on these major themes relevant for 
computational neuroscience research:

Neurobiology of Neural Systems - the anatomy, physiology, and 
behavior of systems of neurons, with emphasis on basic phenomenology.

Advanced Measurement Tools in Neuroscience - Advanced imaging and 
recording techniques reflecting the impact of experimental physics on 
neuroscience.

Algorithms for the Analysis of Neural Data - New algorithms and 
techniques for analyzing data obtained from physiological recording

Theoretical Basis for Collective Neural Dynamics - 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.

Applications

On-line applications: http://neurograd.ucsd.edu/admissions/index.html

The deadline for completed application materials, including letters of
recommendation, is December 15, 2008.

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Participating Faculty include:

* Henry Abarbanel (Physics): Nonlinear and oscillatory dynamics;
modeling central pattern generators in the lobster stomatogastric
ganglion.  Director, 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 (Neurobiology): Regulation synaptic components, assembly
and localization, function and long-term stability.
* Ed Callaway (Salk Institute): Organization and function of neural 
circuits, visual cortex, genetic & viral methods
* Gert Cauwenberghs (Biology):  Neuromorphic Engineering; analog VLSI
chips; wireless recording and nanoscale instrumentation for neural
systems; large-scale cortical modeling.
* Andrea Chiba (Cognitive Science): Spatial attention, associative 
learning, cholinergic, amygdala
* EJ Chichilnisky (Salk Institute):  Retinal multielectrode recording;
neural coding, visual perception.
* Garrison Cottrell (Computer Science and Engineering): Dynamical
neural network models and learning algorithms
* Virginia De Sa (Cognitive Science): Computational basis of perception
and learning (both human and machine); multi-sensory integration and
contextual influences.
* Mark Ellisman (Neurosciences, School of Medicine): High resolution
electron and light microscopy; anatomical reconstructions. Director,
National Center for  Microscopy and Imaging Research
* Fred Gage (Salk Institute): Plasticity, neurogenesis, genetics, 
genomics.  Models of neurogenesis in the hippocampus.
* Tim Gentner (Psychology): Neuroethology of vocal communication and
audition.  Models of birdsong learning.
* Robert Hecht-Nielsen (Electrical and Computer Engineering): Neural
computation and the functional organization of the cerebral cortex.
Founder of Hecht-Nielsen Corporation
* Steve Hillyard (Neurosciences, School of Medicine): EEG, perception,
attention, memory, ERP, SSVEP
* 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 rats; properties of
neuronal assemblies; optical imaging of large-scale activity.
* William Kristan (Neurobiology):  Computational Neuroethology; 
functional and developmental studies of the leech nervous system, including
studies of the bending reflex and locomotion.  Director, Neurosciences
Graduate Program at UCSD
* Herbert Levine (Physics): Nonlinear dynamics and pattern formation
in physical and biological systems, including cardiac dynamics and the
growth and form of bacterial colonies
* Scott Makeig (Institute for Neural Computation): Analysis of cognitive
event-related brain dynamics and fMRI using time-frequency and 
Independent Component Analysis
* Javier Movellan (Institute for Neural Computation): Sensory fusion
and learning algorithms for continuous stochastic systems
* Mikhael Rabinovich (Institute for Nonlinear Science): Dynamical
systems analysis of the stomatogastric ganglion of the lobster and the
antenna lobe of insects
* Pamela Reinagel (Biology):  Sensory and neural coding; natural scene
statistics; recordings from the visual system of cats and rodents.
* John Reynolds (Salk Institute): Visual attention, cortex, psychophysics,
neurophysiology, neural modeling
* Massimo Scanziani (Biology):  Neural circuits in the somotosensory
cortex; physiology of synaptic transmission; inhibitory mechanisms.
* Terrence Sejnowski (Salk Institute/Neurobiology): Computational
neurobiology; physiological studies of neuronal reliability and
synaptic mechanisms. Director, Institute for Neural Computation
* Tanya Sharpee (Salk):  Statistical physics and information theory
approach to understanding sensory processing. Statistical properties
of natural auditory and visual environments.
* Gabriel Silva (Bioengineering): Functional dynamics of retinal and 
cortical neural networks, glial signaling physiology, neural engineering
* Nicholas Spitzer (Neurobiology):  Regulation of ionic channels and
neurotransmitters in neurons; effects of electrical activity in
developing neurons on neural function. Chair of Neurobiology
* Charles Stevens (Salk Institute): Synaptic physiology; theoretical
models of neuroanatomical scaling.
* Emmanuel Todorov (Cognitive Science): Motor control, stochastic 
optimal control, sensorimotor loops
* Roger Tsien (Chemistry):  Second messenger systems in neurons;
development of new optical and MRI probes of neuron function,
including calcium indicators and caged neurotransmitters

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