Instructor:  Bruno Olshausen, baolshausen@ucdavis.edu, Center for Neuroscience 203C, x7-8749

Course goals:  To acquaint students with mathematical modeling tools used in neuroscience and psychology, and to provide "hands-on" experience in using these models.

Grading:  Based on lab assignments (80%) and midterms (20%). Each week there will be a lab assignment to be performed in Matlab.  The nine highest scored assignments (out of a total of ten for the quarter) will be used to determine the final grade.

Readings:  Based primarily on handouts. Some recommended texts include:

Theoretical Neuroscience by Dayan and Abbott (MIT Press) - A comprehensive text, provides a thorough treatment of major modeling concepts in computational neuroscience.

Introduction to the Theory of Neural Computation by Hertz, Krogh, and Palmer (Addison-Wesley) - An excellent reference for many of the standard neural network models.  Although heavy on the math at times, it is nevertheless well-written and quite accessible.

Parallel Distributed Processing by Rumelhart and McClelland (MIT Press) - A classic text on neural network or "connectionist" models, written mainly from a psychological/cognitive science perspective.

Neural Networks for Pattern Recognition by Bishop (Oxford) - Detailed and comprehensive, written mainly from a computer science/statistics perspective.

Information Theory, Pattern Recognition and Neural Networks by Mackay - An excellent on-line text, lucidly written.  Highly recommended.

Spikes by Rieke, Warland, de Ruyter van Stevenick, and Bialek (MIT Press) - A wonderful treatise providing an information-theoretic framework for understanding neural spike trains.

Foundations of Vision by Wandell (Sinauer) - A first rate presentation of major topics in vision science, from the physiology of the retina to computational models.

Vision by Marr (Freeman) - A classic work, especially the first two chapters.

The Science of Musical Sound by Pierce (Freeman) - A good source for the stuff we will cover on sound.