CIS 6930/4930, Introduction to Computational Neuroscience, Spring 2016

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.

Announcements:

As announced in class, the deadline for Q4. of Assignment 3 is April 19th in class. This is also the deadline for the submission of all late assignments (which will be graded with a suitable penalty)

List of Topics covered

Week	Topic	Additional Reading	Assignment
Jan 03 - Jan 09	Adaptation example: Video. Change Blindness examples: Rensink's Website. Change Blindness examples O'Regan's Example-1. Change Blindness examples O'Regan's Example-2. Basic Neurobiology. Powerpoint Slides.	Human brain in numbers
Jan 10 - Jan 16	Our Animated Neuroscience site. Cochlea Retina http://www.retinalmicroscopy.com/ Firing of On-center and Off-center Retinal Ganglion cell. Motor and Somatosensory Cortex Spinal Chord
Jan 17 - Jan 23	Paper on "in vivo" recording. Paper on "slice" recording. Paper on "culture" recording. Paper on "fmri" recording. Neuro Electronics. Powerpoint slides can be found here.		Assignment 1. Due on Jan 26th. The tex file.
Jan 24 - Jan 30	Neuro Electronics continued. Derivation of Goldman-Hodgkin-Katz Eqn Simplification to Nernst Eqn Synaptic transmission
Jan 31 - Feb 06	Neuro Electronics continued. Phenomenological model of synapse Passive membrane Hodgkin Huxley Equations
Feb 07 - Feb 13	Hodgkin Huxley Equations continued Reduced models of the neuron.	Which Model to Use for Cortical Spiking Neurons? By E. M. Izhikevich Link here. Hardware implementations of the above models. Link here Scholerpedia article on the FitzHugh-Nagumo Model: Link here.	Assignment 2. Due on Feb 23rd.
Feb 14 - Feb 20	Analysis of Sensory Systems; objectives Dirac Delta function Generalized functions (distributions and tempered distributions) Spike count rate, instantaneous spike rate.
Feb 21 - Feb 27	Causal, Time Invariant, Fading memory, Bounded memory Mean value thms Taylor approximation, Weirstrass Thm	Causal, Time Invariant, Fading memory systems and Volterra series paper by Boyd and Chua here.
Feb 28 - Mar 05	SPRING BREAK
Mar 06 - Mar 12	Volterra Weiner Series expansion System Identification Stimulus Reconstruction
Mar 13 - Mar 19	Spike triggered averaging Fourier series. Convolution Theorem
Mar 20 - Mar 26	Information Theory and applications to spike timing Entropy, Conditional Entropy and Mutual Information Perceptron and learning rule
Mar 27 - Apr 02	Convergence of Perceptron learning rule Error-Backpropagation in Multilayer networks	Proof of error bound for perceptron here.	Assignment 3. Due on Apr 7th. (New deadline for Q4. Apr 19th).
Apr 03 - Apr 09	Error backpropagation continued. Discussion of assignment Error backpropagation in "Spiking Neuron" Networks
Apr 10 - Apr 16	Recurrent Neural Networks Cohen-Grossberg-Hopfield Nets and associative memory
Apr 17 - Apr 23	Abstract Dynamical System for recurrent spiking neuronal networks Discrete time dynamical system: Logistic map Review