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Computational Neuroscience Curriculum


The goal of the Computational Neurobiology Graduate Program at UCSD is to train researchers that are equally at home with behavioral methods, electrophysiology, statistical tools for data analysis and developing models for brain function. Projects can extend from single cell dyanmics through large-scale imaging of dynamics across the nervous system.

Training activities take five forms: (i) Formal course work and (ii) Journal clubs, (iii) Student presentations, (iv) Research rotations, and (v) Dissertation work.

General Neuroscience Graduate Courses

Students in the Computational Neuroscience Specialization will complete the core series of courses. These include:
  • NEU 200A - Cellular Neuroscience
  • NEU 200B - Systems Neuroscience
  • NEU 200C - Cognitive Neuroscience
  • NEU 257 - Mammalian Neuroanatomy

Specialized Computational Neuroscience Graduate Courses

Students in the Computational Neuroscience Specialization will further complete these three courses:
  • Neurodynamics (BGGN 260 / BENG 260/ PHYS 279 taught by Abarbanel, Cauwenberghs or Silva) - Anatomy, physiology, and electrical and chemical dynamics of individual neurons. Neuromorphic models.
  • Biophysical Basis of Neuronal Computation (PHYS 278 taught by Kleinfeld or Sharpee) - Collective properties and dynamics of neuronal systems, with emphasis on feedforward networks, associative networks, and networks of coupled oscillators.
  • Algorithms for the Analysis of Neural Data (COG 260 / NEU 282 taught by Mukamel) - Characterization of spiking and continuous processes (ECoG, LFP, MEG, fMRI).
  • Elective Courses

    Students are encouraged to take reading classes as well as additional classes in Engineering, Mathematics and Physics to supplement their backgrounds in quantitative skills and measurement techniques.

    Suggested reading classes:
    • BGGN 246 - Computational neurobiology reading course (Sejnowski)
    • NEU 221 - Advanced topics in neurosciences (various faculty)

    Suggested classes in analysis and applied mathematics include:
    • ECE 250 - Parameter estimation
    • ECE 255 - Information theory
    • MATH 250 - Differential geometry
    • MATH 280 - Probability theory
    • MATH 282 - Applied statistics
    • MATH 287B - Multivariate analysis
    • PHYS 210 - Nonequilibrium statistical mechanics
    • PSYC 231 - Data analysis in Matlab

    Suggested classes in engineering and physics include:
    • BENG 278 - Magnetic resonance imaging
    • BENG/ECE 247A - Advanced biophotonics
    • BENG/ECE 247B - Bioelectronics
    • PHYS 270A - Experimental techniques for quantitative biology
    • PHYS 270B - Quantitative biology laboratory
    • ECE 240 - Lasers and optics
    • NEU 259 - Workshop in electron microscopy

    Teaching Requirements

    The best way to learn neurosceince is to TA undergraduate or graduate classes. Graduate students are expected to TA one, or more, course during their graduate career.