Theoretical Neuroscience
Computational and Mathematical Modeling of Neural Systems

Peter Dayan and LF Abbott

Teaching Support
Errata:   pdf  ps


Figures are available in powerpoint format (ppt) or as gzip-ed, tar-ed, collections of png (portable network graphics) files.

ppt     tar.gz    Complete collection of figures

Part I:     Neural Encoding and Decoding

ppt     tar.gz     1   Neural encoding I: Firing rates and spike statistics
ppt     tar.gz     2   Neural encoding II: Reverse correlation and visual receptive fields
ppt     tar.gz     3   Neural decoding
ppt     tar.gz     4   Information theory

Part II:   Neurons and Neural Circuits

ppt     tar.gz     5   Model neurons I: Neuroelectronics
ppt     tar.gz     6   Model neurons II: Conductances and morphology
ppt     tar.gz     7   Network models

Part III:  Adaptation and Learning

ppt     tar.gz     8   Plasticity and learning
ppt     tar.gz     9   Classical conditioning and reinforcement learing
ppt     tar.gz     10 Representational learning

ppt     tar.gz     Mathematical appendix


The complete set of references in the book is available as
pdf    ps.gz    latex


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GNU Octave is free software whose language is mostly compatible with Matlab.

Many tutorials for Matlab can be found on the web (eg using a google search). Many books are also available.