Thanks to Ken and Bernardo for their attempts to answer my question, but I was apparently unclear as to what I meant by "computational neuroscience".
The tools Ken and Bernardo suggest provide means to analyze data from neuroscience research, but I'm actually looking for means to simulate biologically realistic neural systems. Maybe it would help if I provided some keywords, so here is a list of chapters/sections from MATLAB section of the book "How the brain computes: Network fundamentals of computational neuroscience" by Thomas Trappenberg: 12 A MATLAB guide to computational neuroscience 12.1 Introduction to the MATLAB programming environ- ment ... 12.2 Spiking neurons and numerical integration in MAT- LAB 12.2.1 Integrating Hodgkin-Huxley equations with the Euler method 12.2.2 The Wilson model and advanced integration 12.2.3 MATLAB function files 12.2.4 Leaky integrate-and-fire neuron 12.2.5 Poisson spike trains 12.2.6 Netlet formulas by Anninos et al. 12.3 Associators and Hebbian Learning 12.3.1 Hebbian Weight matrix in rate models 12.3.2 Hebbian learning with weight decay 12.4 Recurrent networks and networks dynamics 12.4.1 Example of a complete network simulation 12.4.2 Quasi-continuous attractor network 12.4.3 Networks with random asymmetric weight ma- trix 12.4.4 The Lorenz attractor 12.5 Continuous attractor neural networks 12.5.1 Path-integration 12.6 Error-backpropagation network -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University www.thatmike.com Looking to arrange a meeting? Check my public calendar: http://www.thatmike.com/mikes-public-calendar ~ Certainty is folly... I think. ~ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
