Overview

The goal of the Computational Neurobiology Graduate Program at UCSD is to train researchers that 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.

Themes

Training activities take five forms: (i) Formal course work and (ii) Journal clubs, (iii) Student presentations, (iv) Research rotations, and (v) Dissertation work. All of these activities are built around four major themes of research.

1. Neurobiology of Neural Systems - the anatomy, physiology, and behavior of systems of neurons, with emphasis on basic phenomenology.
2. Advanced Measurement Tools in Neuroscience - Advanced imaging and recording techniques reflecting the impact of experimental physics on neuroscience.
3. Algorithms for the Analysis of Neural Data - New algorithms and techniques for analyzing data obtained from physiological recording (e.g. multi-taper spectral analysis, indepedent component analysis)
4. A 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.

Teaching Requirements

The best way to learn the biology is to TA undergraduate classes. Students are expected to TA three courses during their graduate career, including one Biology department course.

Courses

Students in the graduate program will complete the core Neuroscience series of courses. These courses include:

• Neurosciences 200A - Cellular neuroscience
• Neurosciences 200B - Systems neuroscience
• Neurosciences 200C - Cognitive neuroscience
• Neurosciences 241 - Scientific ethics
• Neurosciences 257 - Mammalian neuroanatomy
• Neurosciences 276 - Research rounds (every quarter, years 1-2)

• Biology 260 - Nonlinear dynamics of neural systems
• Cognitive Science 260 - Neural networks
• Physics 271 - Biophysics of neurons and networks
• Biology 246 - Computational neurobiology journal club (3 quarters)
• Biology 266 - Advanced neurobiology laboratory [imaging and electrophysiology]

• 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

Qualifying Exam

At the end of the first year, students are required to take a combined written and oral comprehensive examination, as written by the program faculty to determine areas of strength and weakness. Students who do not pass the examination will either be allowed to retake the examination at the end of their second year or be awarded a terminal M.S. degree after completing two years in the program.

There are four broad areas of examinations.

1. Mathematics, Physics & Engineering
2. Molecular, Cellular & Development
3. Systems, Behavior & Cognition
4. Modeling, Computation & Theory

Each section consists of a selection of questions. Each student selects a single question from each area and writes a 2 page essay, concisely summarizing a large body of work and drawing out the important organizing principles from specific research journal articles.

There is a 30 minute oral exam on each question, which begins with a 5-10 minute summary of the essay and is followed by questions from the faculty. In addition to probing specific issue raised by the question chosen by the student, more general issues are also explored.

After the examinations the faculty meets separately with each student to provide feedback on strengths and weaknesses and to make recommendations for additional courses and training.