Fundamentals of Computational Neuroscience (2nd Edition)

The second edition of Fundamentals of Computational Neuroscience is now available at Oxford University Press and Amazon.ca.

Programs

A full list of programs used in the book is available here.

A full list of figures from the book is available here.

Below are course slides (pdf format). AT this time there are short versions with the most important points for each chapter.
Chapter1.pdf
Chapter2.pdf
Chapter3.pdf
Chapter4.pdf
Chapter5.pdf
Chapter6.pdf
Chapter7.pdf
Chapter8.pdf
Chapter9.pdf
Chapter10.pdf

I would appreciate your comments and corrections. Please send email to tt@cs.dal.ca

Page 146, Table 6.1: The Boolean AND function has a zero (0) as first entry in y column instead of the one (1). The displayed function is the Boolean XOR function (or non-XOR, depending on the translation of 0/1 to true/false).

Page 61, Section 3.1.5: Equation (3.20) on the right should read u(v>30) = u+d

Page 131, Section 5.2.3: The numerical example is not correct. The sum of the synaptic events is binominal distributed with mean 10000*0.005=50 and variance 10000*0.005*(1-0.005), which is reasonable well approximated by a Gaussian with this mean and variance.; Important for the argument here is that the `noise' in the average is much less than N times the 'noise' of the single events since some of the fluctuations will go in different directions and will chancel out each other. More formally, the sum of any random numbers with means mu and variances sigma^2 is a random number with mean N*mu and variance N*sigma. Thus, the variation in the background becomes less important when many synapses are involved since the standard deviation of the sum of random variables only scales with the square root of the number of variables.

Page 330, Appendix B.4: This second derivative in the example is (x-t). The term should be substituted in equation B.15 instead of th term (1-x).