Computational Neuroscience and Octave.

Early next year I will be taking the Coursera course "Computational Neuroscience.  Rather than using Python it will use MatLab.  MatLab is an expensive product.  I have an old copy of it on the computer that died about a month ago.  I bought MatLab because I got tired of running Octave in line mode on top of Cygwin.  I didn't want to buy MatLab again just for this one course so I gave Octave another look. 

Octave still has the same look and feel I remember but now there is a GUI wrapper for it.  DomainMath IDE provides that wrapper.  My few minutes with it so far looks promising even though it has a few quirks I haven't figured out yet.  DomainMath IDE is a Java project so you will need to have Java running.  I've running Java version 7 update 45.  I installed Octave in its default directory then I put the DomainMathIDE_v0.1.5.7z file in the same directory and unzipped it using WinRAR.  DomainMathIDE.jar comes up and seems to work just fine even though right now I'm getting the error: "'javaaddpath' undefined".  I'll track this down sooner or later.  While I installed the java library when I installed Octave it appears I don't have it registered quite right.  I haven't been able to make DomainMath IDE the default editor for .m files or even right click on the files and edit them with DomainMath IDE.  I can live with this for now.

WinRAR is worth the $29.  It handles all the strange compression formats I've run across so far.  Maybe 7-zip would work as well.  It certainly would work on DomainMathIDE_v0.1.5.7z.

I bought Tutorial on Neural Systems Modeling so I could get up to speed before class begins.  It turns out it is one of the suggested books for the class.  The website has the source files for the "MATLAB BOXes".  That's certainly easier if you don't need to type code to learn from it.  There are plenty of exercises to get you head into the material.  It doesn't cover cable theory so it looks like I'll be exploring point neuron models for now where spike rate is modeled by a number rather than spikes.  I can live with this an learn.  Later I can move to more realistic models when I'm investigating aspects this model doesn't cover.  For now it's the Rosenblatt model, the Perceptron or a variation of it.