CIS 4930, Artificial Intelligence and Neuroscience, Fall 2024

Instructor:
Arunava Banerjee
Office: MALA 6101.
E-mail: arunava@ufl.edu.
Phone: 908-720-6694.
Office hours: Zoom; Date and Time TBD.

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.

Suggested 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 and relate it to recent advances in artifial intelligence and machine learning. 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 and programming assignments. The last programming assignment is fairly elaborate.

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.

Announcements:

List of Topics covered

Week	Topic	Additional Reading	Assignment
Aug 19 - Aug 25	Introduction
Aug 26 - Sep 01	Color Adaptation: Example. Change Blindness (Flicker): Example 1. Change Blindness (Gradual): Example 2. Selective Attention test: Example. Color Blindness: Example. Illusory Contours: Kanizsa triangle. Curvature Blindness. Optical Illusions: Examples. Dog by Boston Dynamics	Theoretical problem of Gait Shifting Human brain in numbers Isotropic Fractionator MIT Open Courseware
Sep 02 - Sep 08	Number of Neurons in various animals. The human brain BBC News exposition. Basic Neurobiology Powerpoint Slides. Brain Model Physical model Video. Brain Model Animation Video. Started review of Vector Spaces	Theoretical problem of Gait Shifting Human brain in numbers Isotropic Fractionator MIT Open Courseware	One pg synopsis of Isotropic Fractionator paper, due wed 9/11
Sep 09 - Sep 15	Vector Spaces Linear independence, Span Basis Formal description of Multi-variate linear rigression.		Dimensionality Theorem
Sep 16 - Sep 22	Multi-variate linear rigression: Closed form solution
Sep 23 - Sep 29	Support vector machines Primal and dual formulations
Sep 30 - Oct 06	Kernel Support vector machines Neuro Electronics. Powerpoint slides
Oct 07 - Oct 13	Neuro Electronics continued.
Oct 14 - Oct 20	Neuro Electronics continued.
Oct 21 - Oct 27	Finished Neuro Electronics. Leaky integrate and fire model, Spike response model Perceptron