CIS 6930/4930, Introduction to Computational Neuroscience, Spring 2005

Instructor:
Prof. Arunava Banerjee
Office: CSE E336.
E-mail: arunava@cise.ufl.edu.
Phone: 392-1476.
Office hours: Wednesday 2:00 p.m.-4:00 p.m. or by appointment.

Pre-requisites:

• There are no official pre-requisites for this course. However, knowledge of calculus and linear algebra is necessary since we shall be touching on areas such as measure theory, ergodic theory etc. In addition, since there will be several assignments dealing with simulations of neural systems, proficiency in some programming language is a must.

Textbook: Theoretical Neuroscience, Dayan and Abbott, MIT Press, ISBN 0-262-04199-5.
Neuroscience Reference: Fundamental Neuroscieence, Zigmond, Bloom, Landis, Roberts, and Squire, Academic Press, ISBN 0-12-780870-1.

The goal of Computational Neuroscience is to acquire a formal understanding of how the brain (or any part thereof) works. The central dogma is that there are computational principles lurking in the dynamics of systems of neurons in the brain that we can harness to create better machines for such disparate tasks as computer vision, audition, language processing etc (note that in all these cases human beings far surpass the best known solutions).

This course is aimed at giving an overview of the field. In addition to particular issues, we shall take a tour through some essential neurobiology and a couple of mathematical areas. The targeted audience is students who wish to conduct research in this field, although any body interested in acquainting themselves with the area is welcome to attend. Although there will be a text that we shall (loosely) follow (Theoretical Neuroscience by Dayan & Abbott; available as an e-book thru the UF library system), a large portion of the course will involve material from disparate sources (other books, articles etc.)

Please return to this page at least once a week to check updates in the table below

Evaluation: There will be no exams in this course. The final grade will be based on a series of written assignment, programming projects, and a final report that puts the entire thing together. The final report will account for 20% of the grade.

Course Policies:

• Late assignments: All homework assignments are due before class.
• Plagiarism: You are expected to submit your own solutions to the assignments. While the final project and presentation will be done in groups, each member will be required to demonstrate his/her contribution to the work.
• Attendance: Their is no official attendance requirement. If you find better use of the time spent sitting thru lectures, please feel free to devote such to any occupation of your liking. However, keep in mind that it is your responsibility to stay abreast of the material presented in class.
• Cell Phones: Absolutely no phone calls during class. Please turn off the ringer on your cell phone before coming to class.

Academic Dishonesty: See http://www.dso.ufl.edu/judicial/honestybrochure.htm for Academic Honesty Guidelines. All academic dishonesty cases will be handled through the University of Florida Honor Court procedures as documented by the office of Student Services, P202 Peabody Hall. You may contact them at 392-1261 for a "Student Judicial Process: Guide for Students" pamphlet.

Students with Disabilities: Students requesting classroom accommodation must first register with the Dean of Students Office. The Dean of Students Office will provide documentation to the student who must then provide this documentation to the Instructor when requesting accommodation.

List of Topics covered

Week	Topic	Additional Reading	Assignment
Jan 02 - Jan 08	Basic Neurobiology. Powerpoint slides can be found here. Neuro Electronics. Powerpoint slides can be found here.	Readings: Chapters 5 and 6 of the text. Introductory Reading for the lecture can be found here.
Jan 09 - Jan 15	Neuro Electronics continued. Same slides.	Readings: Chapters 5 and 6 of the text.	Assignment 1. Due on Jan 27th.
Jan 16 - Jan 22	Neuro Electronics continued. HH-Eqns, and Synaptic transmission. Reduced rate model of a neuron, Perceptrons.	Readings: Chapters 5 and 6 of the text.
Jan 23 - Jan 29	Perceptron Rule, Energy function, Multi layer perceptron, back-propagation Associative/Recurrent Networks.		Assignment 2. Due on Feb 10th and 17th.
Jan 30 - Feb 05	Introduction to Abstract Dynamical Systems
Feb 06 - Feb 12	Abstract Dynamical Systems continued. Metric spaces Topological spaces
Feb 13 - Feb 19	Metric spaces, Topological Spaces, continued. Contraction mapping theorem
Feb 20 - Feb 26	Feb 22nd, Dr. E. N. Brown's talk in DeWeese Auditorium LG-101A in MBI. Contraction mapping theorem continued, Gerstner's spike response model of the neuron.
Feb 27 - Mar 05	SPRING BREAK
Mar 06 - Mar 12	Dynamical Systems Analysis of Systems of Spiking Neurons. Slides can be found here.
Mar 13 - Mar 19	Dynamical Systems Analysis of Systems of Spiking Neurons. Continued.		Assignment 3. Due on Mar 31st.
Mar 20 - Mar 26	Vector Spaces, Banach Spaces, Hilbert Spaces
Mar 27 - Apr 02	Linear Time Invariant Systems, Non-Linear Time Invariant system and the Volterra expansion
Apr 03 - Apr 09	Information Theory and applications to neuronal systems.	Readings: Chapter 4 of the text.	Assignment 4. Due on Apr 19th.
Apr 10 - Apr 16	The Visual System Probability Theory: Weak and Strong Law of Large Numbers
Apr 17 - Apr 23	Apr 19th, my talk in DeWeese Auditorium LG-101A in MBI. Classes End.