Computational Neurosciences Specialization
Overview
The Computational Neuroscience Specialization is part of the broader Neuroscience Graduate Program at the University of California at San Diego. The specialization is designed to train young scientists with the broad range of scientific and technical skills that are essential to understand the computational resources of neural systems. This program welcomes students with backgrounds in neuroscience, physics, chemistry, biology, psychology, computer science, engineering, and mathematics.
The program includes rigorous course work in both experimental and computational neuroscience. Thesis research must include both an experimental and a computational component, often arranged by the student as a collaboration between two research groups.
For more information, see the student-maintained website for the Computational Neuroscience Specialization.
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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