CIS 6930/4930, Introduction to Computational Neuroscience, Fall 2007

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 assignments, and programming projects.

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
Aug 20 - Aug 25	Change Blindness examples: Rensink's Website. Change Blindness examples O'Regan's Example-1. Change Blindness examples O'Regan's Example-2.
Aug 27 - Sep 01	Basic Neurobiology. Powerpoint slides can be found here. Paper on "in vivo" recording. Paper on "slice" recording. Paper on "culture" recording. Paper on "fmri" recording.		Assignment 1. Due on Sep 11th. The tex file.
Sep 03 - Sep 08	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.
Sep 10 - Sep 15	Neuro Electronics continued. Synaptic transmission.
Sep 17 - Sep 22	Reduced Models of the neuron: Asymptotic behavior of the Leaky Integrate and Fire Model Linearity of the Leaky Integrate and Fire Model The Spike Response Model f-I curve and the sigmoidal neuron	Spiking Neuron Models: Single Neurons, Populations, Plasticity By W. Gerstner and W. M. Kistler.	Assignment 2. Due on Sep 27th.(Deadline extended to next tuesday)
Sep 24 - Sep 29	System Identification: Time Invariant Systems Taylor's theorem and proof Linear Time invariant systems and convolution kernels
Oct 01 - Oct 06	System Identification continued The impulse response Non-Linear Time Invariant Systems and the Volterra expansion Vector Space and Normed Vector Space Inner Product space
Oct 08 - Oct 13	Fourier Series decomposition Convolution Theorem		Assignment 3.
Oct 15 - Oct 20	Reduced model of the neuron Supervised Learning Perceptron learning algorithm
Oct 22 - Oct 27	Error Backpropagation algorithm Hopfield net Proof of fixed points using Lyapunov function
Oct 29 - Nov 03	Basic Point set topology Open and Closed Sets Metric spaces		Assignment 4.
Nov 05 - Nov 10	Dynamical Systems theory Contraction Mapping theorem
Nov 12 - Nov 17	The tent map Sensitive dependence on initial conditions Wandering, nonwandering points, and attractors Topological conjugacy
Nov 19 - Nov 24	Review of assignment		Assignment 5.
Nov 26 - Dec 01	Derivation of perturbation law for spikes Theorem for sensitive dependence for recurrent cortical networks