VS298 (Fall 06): Additional resources

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Here is a collection of additional material that you may find useful for this course.

MATLAB Resources

This course will require basic familiarity with MATLAB. The program itself has an extensive help system and tutorials.

GNU Octave

Octave is free program which aims to be compatible with MATLAB. If you are clever, you can get by using Octave if you become familiar with some of its quirks. Haven't met anyone who has opted to take this route.

Feel free to use R or Mathematica if that is what you are most comfortable with.

Additional Textbooks


  • Kandel, E.R. and Schwartz, J.H. and Jessel, TM. Principles of Neuroscience. McGraw-Hill, 2000.
    • Standard neuroscience text, an encyclopedic collection covering the main topics in neuroscience. Little theory.
  • Koch, C. Biophysics of Computation: Information Processing in Single Neurons. Oxford University Press, 1998.
    • Explores in rich detail experimental and theoretical findings in single neuron biophysics. Compartment models, cable equation.
  • Rieke, F. and Bialek, W. and Warland, D. and Van Steveninck, R.R. Spikes: Exploring the Neural Code. Bradford Book, 1999.
    • Very readable account of methods used to characterize how neurons encode stimuli, with detailed mathematical appendix.

Neural networks

  • Arbib, M.A. The Handbook of Brain Theory and Neural Networks, 2nd Edition. The MIT Press, 2002.
    • An encylopedic collection of short articles written by leading researchers about anything you could imagine related to neural networks and the brain. Mostly theory and computation.
  • Bishop, C. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
    • A standard text on neural networks from the perspective of statistical pattern recognition.
  • Duda, R.O. and Hart, P.E. and Stork, D.G. Pattern classification. Wiley, 2001.
    • The updated version of this classic text covers many recent topics in statistical learning theory and neural networks.

Probability and computational math

  • Cover, T.M. and Thomas, J.A. Elements of information theory. Wiley, 1991.
    • Standard text on information theory. Complete proofs of standard results.
  • Papoulis, A. Probability, Random Variables and Stochastic Processes. McGraw-Hill, 2002.
    • An excellent, accessible resource for many topics in basic probability.
  • Press, W.H. and Teukolsky, S.A. and Vetterling, W.T. and Flannery, B.P. Numerical recipes in C: the art of scientific computing. Cambridge University Press, 1992. Available online
    • Standard scientific computing text. Clear, brief explanations of theory as well as practical aspects of numerical methods.

Related courses at Berkeley

  • TCN: Computational neuroscience journal club
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