Computational Neurobiology Specialization
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.
Note: The Computational Neurobiology
Specialization is nearly finalized as
of September 2007, but it awaits formal
endorsement by a university committee,
expected by early 2008.
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.
- lgorithms
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.
Requirements
Accordingly, in addition to the broader
Neuroscience program requirements (Theme
1), students are required to take the
following course sequence:
- PHYS
271. Biophysics
of neurons
and networks
(Kleinfeld/Levine)
- BGGN
260. Neurodynamics
(Cauwenberghs/Abarbanel)
- GGN
266. Advanced
imaging
and electrophysiology lab (Kleinfeld)
At the end of each requried course,
an oral exam is administered by the instructor
and one other faculty member to test
the student's mastery of the area.
The Computational Neurobiology journal
club is also strongly recommended:
- BGGN
246 Computational Neurobiology journal
club (Sejnowski)
Finally, because of the mathematical
rigor of the program, students are encouraged
to take additional classes in engineering,
mathematics and physics to supplement
their backgrounds as needed. Sample classes
students have taken include:
- ECE
101 -
Linear systems
- ECE
161 -
Digital
signal
processing
- ECE 250 -
Parameter
estimation
- ECE
255 -
Information
theory
- Physics
210 -
Nonequilibrium
statistical
mechanics
- Math
180 -
Introduction
to probability
- Math
250 -
Differential
geometry
- Math
280 -
Probability
theory
- Math
281 -
Mathematical
statistics
- Math
285A
- Introduction
to stochastic
processes
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