CIS 6930/4930, Introduction to Computational Neuroscience, Spring 2006

Instructor:
Prof. Arunava Banerjee
Office: CSE E336.
E-mail: arunava@cise.ufl.edu.
Phone: 392-1476.
Office hours: Tuesday 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. 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 09 - Jan 15	Basic Neurobiology. Powerpoint slides can be found here. Sample paper can be found here here.
Jan 16 - Jan 22	Paper on "in vivo" recording. Paper on "slice" recording. Paper on "culture" recording. Paper on "fmri" recording.		Assignment 1. Due on Jan 30th. The tex file.
Jan 23 - Jan 29	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 30 - Feb 05	Neuro Electronics continued.		Assignment 2. Due on Feb 13th.
Feb 06 - Feb 12	Reduced Models of the Neuron	Spiking Neuron Models: Single Neurons, Populations, Plasticity By W. Gerstner and W. M. Kistler.
Feb 13 - Feb 19	Perceptron learning rule Multi-Layer Perceptron learning rule (Error back-propagation)	Introduction to Machine Learning By N. J. Nilsson. Read Sec 4.1 and 4.4.	The deadline for Assignment 2 has been extended to Monday Feb 20th.
Feb 20 - Feb 26	Hopfield Nets Introduction to spaces (topological thru inner-product)	Hopfield's 1982 Paper.	Assignment 3. Due on Mar 1st.
Feb 27 - Mar 5	Spaces Continued Topological Space: open and closed sets Metric Spaces: contraction mapping theorem Vector Space, Banach Space, Hilbert Space.
Mar 6 - Mar 12	Fourier Series Linear Time Invariant Systems		Assignment 4. Due on Mar 22nd.
Mar 13 - Mar 19	SPRING BREAK
Mar 20 - Mar 26	Linear/non-linear Time Invariant Systems continued Convolutions, Kernels, etc. Volterra Series
Mar 27 - Apr 2	Early visual system--- Photoreceptors, spatio-temporal kernels for ganglion cells, LGN cells, Simple cells in V1. Optimal linear kernel, spike triggered average, stimulus response correlation, white noise signal, etc.	Readings: Chapters 1 and 2 of the text.
Apr 3 - Apr 9	Dynamical Systems theory
Apr 10 - Apr 16	Dynamical Systems theory continued		Assignment 5. Due on Apr 17th.
Apr 17 - Apr 23	Abstract Dynamical System for Network of Spiking Neurons		Assignment 6. Due on May 1st.
Apr 24 - Apr 30	Information Theory