Computational Neuroscience, Fall 2012

The course is for 3rd year undergraduates and graduate students of CS, and is an introduction to mathematical and computational models in the area of brain research.


1.           Computer Science and Computational Neuroscience – why are they related

2.           Introduction to the brain physiology: synapsed to centers

3.           Early models of Neural Networks

4.           Introduction to MATLAB

A brief vide introduction on Matlab and Digital Signal Processing:


5.           Introduction to Image Processing in the Biological context

6.           Basic models of single neurons

Formal Neuron Models:

(From the book by Gerstner and Kistler:
Spiking Neuron Models. Single Neurons, Populations, Plasticity 
Cambridge University Press, 2002


Comparison between several models: (Please see the full text PDF and if you like there is a nice matlab code example that reproduces the results in the figure)


Lecture notes


7.           Plasticity and learning

Synaptic Plasticity (Hebbian): 


    lecture notes


8.           Models of stereoscopic vision

9.           Machine Learning and CNS 1

Machine Learning and fMRI (tools of the trade):

Predicting Choices:


    Lecture notes


10.       Machine Learning 2

Machine Learning and fMRI 2 (going beyond):


11.       Reinforcement Learning crash course:


Lecture notes


Final project      Starter code and images


Course handouts: part 1 part 2  part 3 part 4 part5


Mid Term project

Course projects: Omri Perez