CSCI 6508 Neurocomputing

Fundamentals in Computational Neuroscience

 
     
News:

Nov 1: Draft of CANN chapter

Oct 28: CANN program: cann.m hebb.m in_signal_pbc.m rnn_ode_u f1.m

Oct 26: som.m program

Oct. 19: Final Project 4 due November 23

Oct 5: Project 3 due October 19

Sept 30: Project 2 due October 5

Sept 21: Paper by Eugene Izhikevich comparing different models of spiking neurons

Sept 20: Project 1 due September 28 in class.

Sept14: Function to disply patter patternDisp.m

Sept14: Here's the promissed matrix multiplication. Note that you need three loops!

Sept.14: Work on the exercises. We will talk about them next week.

Sept.8: The first class is on Sept. 9 in seminar room 2; thereafter it is in room 311.

 
Outline:

Human cognitive abilities have long guided the development of computer systems. This course introduces the principles of information-processing in the brain, including the functionality of single neurons, networks of neurons, and large-scale neural architectures for specific cognitive functions. We discuss the information representation in the brain (distributed versus localist), information theoretical studies of spiking neurons, synaptic plasticity and adaptive architectures, and various forms of memory. We will also study some specific mental abilities and some research topics (time permitted) such as vision, motor control, navigation, sleep, and consciousness. These issues will be contrasted with approaches that are discussed in the machine learning literature. The course includes a MATLAB tutorial, and the students are encouraged to explore some of the functionalities of basic neural networks.

This is a research-oriented course an requires full participation. Students are required to read the textbook so that the class time can be dedicated to discussions.Each student must present and discuss a journal paper, and each student has to work on a major project leading to a course paper.

 
Instructor: Dr. Thomas Trappenberg  
Office Hours: Monday/Friday 11:45-12:45  
Textbook

Thomas P. Trappenberg

Fundamental of Computational Neuroscience

Oxford University Press, ISBN 0-19-851583-9

Programs of the book

 
Grading Scheme:

100% Projects (individual and group projects)

 
     
Honesty:

It is important for students to be aware of the Intellectual Honesty regulations at Dalhousie University. Please see http://plagiarism.dal.ca/ for more information. Any suspected cases of plagiarism will be forwarded to the Senate Discipline Committee.