Electrical and Computer Engineering 768
Special Topics in Signal Processing:
Models of the Neuron

Objective:

To provide a solid conceptual and quantitative background in the modeling of biological neurons and how they function as computational devices.  Practical experience will be gained in modeling neurons from a number of perspectives, including equivalent electrical circuits, nonlinear dynamical systems, and random point-processes, and an introduction to the mathematics required to understand and implement these different engineering methodologies will be given.

Instructor:

Dr. Ian Bruce,
CRL 229, Ext. 26984.
ibruce@mail.ece.mcmaster.ca

Text:

C. Koch, Biophysics of computation: information processing in single neurons, Oxford University Press, 1999. (ISBN: 0195104919)

References:

P. Dayan and L. F. Abbott, Theoretical neuroscience, MIT Press, 2001. (ISBN: 0262041995)

D. Johnston and S. M.-S. Wu, Foundations of cellular neurophysiology, MIT Press, 1994. (ISBN: 0262100533)

C. Koch and I. Segev, Methods in neuronal modeling - 2nd edition, MIT Press, 1998. (ISBN: 0262112310)

F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek, Spikes: exploring the neural code, MIT Press, 1996. (ISBN: 0262181746)

D. L. Snyder and M. I. Miller, Random point processes in time and space, Springer-Verlag, 1991. (ISBN: 0387975772)

S. H. Strogatz, Nonlinear dynamics and chaos: with applications in physics, biology, chemistry, and engineering, Perseus Books, 2001. (ISBN: 0738204536)

Prerequisite:

A basic undergraduate understanding of electrical circuits, linear systems, ordinary and partial differential equations, probability and random processes.

Course Outline:

Introduction to Biological Neurons and Neural Computation (1 Lecture)

Basic anatomy and physiology of neurons, membrane potential, spiking, spike propagation, synapses, excitation and inhibition, basics of neural computation;

Simple Deterministic Models of Neural Excitation (2 Lectures)

Integrate-and-fire models, discharge-rate models, simple neural networks;

Stochastic Models of Neural Activity (2 Lectures)

Poisson- and renewal-process models, random-walk models;

Nonlinear Dynamical Models of Neural Excitation (4 Lectures)

The Hodgkin-Huxley model, ionic channels, activation and inactivation states, action potential generation, phase-plane analysis of neural excitability, nonlinear dynamics;

Axons and Dendritic Trees (2 Lectures)

Linear cable theory, modeling dendritic trees, action potential propagation, compartmental models.

Assessment:

Assignments (60% ); Midterm (20%); Final (20%).

Term:

I.

Lectures:

There will be eleven 3-hour lectures, with the possibility of one extra, if required.

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Created by Ian Bruce <ibruce@ieee.org> - last modified Friday 29 November, 2002