CIS 6930, Introduction to Computational Neuroscience, Spring 2018

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 TWO 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:

• HW 1 (due Tue 1/16) announced.
• HW 2 (due Thu 2/2) announced.
• HW 3 (Programming Assignment 1) (due Tue 2/27) announced.
• Project due Wednesday April 25th. Submission later than that will be considered late. Also, the final deadline for late submissions is April 30th.

List of Topics covered

Week	Topic	Additional Reading	Assignment
Jan 07 - Jan 13	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. Curvature Blindness. Dog by Boston Dynamics Quadruped with wheels by Boston Dynamics Number of Neurons in various animals.	Theoretical problem of Gait Shifting Human brain in numbers Isotropic Fractionator	One pg synopsis of Isotropic Fractionator paper, due tue 1/16
Jan 14 - Jan 20	Basic Neurobiology Powerpoint Slides. Our Animated Neuroscience site (Senior Projects) Cochlea and cross section Retina http://www.retinalmicroscopy.com/ Firing of On-center and Off-center Retinal Ganglion cell. Motor and Somatosensory Cortex Spinal Chord	Paper on "slice" recording. Paper on "culture" recording. Paper on "fmri" recording.
Jan 21 - Jan 27	Cellular structure of cerebral cortex The linear-nonlinear-Poisson cascade model Receptive field	Paper on "in vivo" recording. and supporting Article on Reverse Correlation	One pg synopsis of Characterizing Receptive Field paper, due thu 2/2
Jan 28 - Feb 03	Case for and against the LNLP model of the neuron Poisson point process	Paper on reliability of spike generation. Precise spike timing in primate retinal ganglion cells. And an earlier Similar well referenced paper.
Feb 04 - Feb 10	Neuro Electronics. Powerpoint slides
Feb 11 - Feb 17	Neuro Electronics. Continued Powerpoint slides Synaptic transmission Isopotential neuron ODE models and solutions
Feb 18 - Feb 24	Neuro Electronics. Continued Powerpoint slides Hodgkin Huxley Equations		Programming Assignment 1. Due on Feb 27th.
Feb 25 - Mar 03	Hodgkin Huxley Equations continued Reduced models of the neuron. Leaky-integrate and fire Spike response	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.
Mar 04 - Mar 10	SPRING BREAK
Mar 11 - Mar 17	Analysis of Sensory Systems; objectives System Identification Spike count rate, instantaneous spike rate. Taylor approximation, Weirstrass Theorem
Mar 18 - Mar 24	Causal, Time Invariant, Fading memory, Bounded memory Dirac Delta function Volterra Weiner Series expansion	Causal, Time Invariant, Fading memory systems and Volterra series paper by Boyd and Chua here.	Assignment 3. Due on Apr 3rd.
Mar 25 - Mar 31	Information Theory and applications to spike timing Entropy, Conditional Entropy and Mutual Information
Apr 01 - Apr 07	Abstract Dynamical System theory Discrete time and Continuous time dynamical systems Fixed Points, Periodic orbits Stable, Unstable, Neurtral Linear dynamical system
Apr 08 - Apr 14	Logistic map Non-wandering set, transient set Sensitive dependence on initial conditions Markov partition and Attractors Overview of Abstract Dynamical System for recurrent spiking neuronal networks
Apr 15 - Apr 21	Long term potentiation, Long term depression Spike time dependent plasticity Simplified model of neuron for learning Perceptron and learning rule Convergence of Perceptron learning rule Error-Backpropagation in Multilayer networks	Proof of error bound for perceptron here.
Apr 22 - Apr 28	Review