CIS 6930, Introduction to Computational Neuroscience, Spring 2018

Place:CSE; E222
Time:Tuesday 7 (1:55-2:45 p.m.) and Thursday 7,8 (1:55-3:50 p.m.)

Prof. Arunava Banerjee
Office: CSE E336.
Phone: 392-1476.
Office hours: Wednesday 2:00 p.m.-4:00 p.m. or by appointment.


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:

Academic Dishonesty: See 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 07 - Jan 13 One pg synopsis of Isotropic Fractionator paper, due tue 1/16
Jan 14 - Jan 20
  • Paper on "slice" recording.
  • Paper on "culture" recording.
  • Paper on "fmri" recording.
Jan 21 - Jan 27
  • 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
Feb 04 - Feb 10
Feb 11 - Feb 17
  • Neuro Electronics. Continued Powerpoint slides
  • Synaptic transmission
  • Isopotential neuron
  • ODE models and solutions
Feb 18 - Feb 24
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.
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