BioE 472: Neural Modeling

Instructor:

James Patton, Associate Prof. of BioE

Office: SEO 222, phone 413-7664

Email: pattonj@uic.edu (best way to reach)

Required text:

· Foundations of Cellular Neurophysiology, by Johnston and Wu (³3^rd printing corrects errors in earlier printings)

· Some supplemental reading will be provided

Suggested reference texts:

· Ion Channels of Excitable Membranes, by B. Hille

· Cellular Biophysics, Vol.1& 2, by T. Weiss

· Methods in Neuronal Modeling, 1989 Koch & Segev, eds.

· Biophysics of Computation: Information Processing in Single Neurons, by Cristof Koch

· Electric Current Flow in Excitable Cells, by Jack, Noble, and Tsien

· Principles of Neural Science, Kandel et al.

· Mastering Matlab, Hanselman and Littlefield (version to match that of the software you use)

Prerequisite(s):

· (BioE 310 or ECE 310 or ME 312) and credit or concurrent registration in (BioS 484 or BioS 485)

· or equivalent circuits, physiology, & diff. equations

Grading:

30% for 2 hourly exams (15% each)

40% Homework (includes computer simulations)

30% Final Project, consisting of:

10% Content

10% Presentation

10% Writeup

Graduate students will additionally each present a lecture to the class on a scientific paper.

Main Objectives:

· Students should develop a fundamental understanding of the physical processes responsible for the information flow through neurons.

· Students should develop a fundamental understanding of the linear and nonlinear behavior of neurons, and the quantitative methods used to simulate their behavior.

· Students are expected to recognize the probabilistic nature of many neuronal processes, and be able to apply both deterministic and stochastic approaches to studying them.

· Students should master simple mathematical representations of diffusion, dendrites, somas, axons, membranes, ion channels, vesicular release and receptors, and be able to assemble these together into a model.

· Students should understand the basis of synaptic communication between neurons, and be able to use mathematical models to predict the behavior of 2 neurons in series or loop.

· Students can demonstrate their knowledge through the practice of MATLAB programming.

Course Outline:

· Ion movements in salt water compartments and across membranes (J&W Ch. 2)

· Electrical properties of graded-response and excitable membranes (J&W Ch. 3)

· Compartmental models of the neuron (J&W Ch. 4/Ch. 13)

· Nonlinear properties of excitable membranes (J&W Ch. 5)

· Hodgkin-Huxley model (J&W Ch. 6)

· Core conductor (cable) theory (J&W Ch. 4)

· Voltage-gated ionic conductances (J&W Ch. 7-8)

· Stochastic analysis of single channels (J&W Ch. 9-10)

· Synaptic transmission: Pre and post synaptic mechanisms (J&W Ch. 11, 12 13)

NOTE: in the past, class has only made it thru Chap 10

Coursework Policies and Academic Conduct:

· This course will use BlackBoard to communicate:

http://blackboard.uic.edu/

· Homework is due by 5 pm on the due date, and to be handed in via the Blackboard Web Interface or, for items impossible to do electronically, at the Patton mailbox in 222 SEO.

· Late homework may be handed in for reduced credit (5% per day, for a maximum of 75% off).

· All assignments (homework, exams, papers etc.) are to be solely the work of the individual unless stated otherwise. This means:

o Exams: No giving or receiving of any information, of any kind, during exams, but 8.5 by 11 inch crib sheets are allowed

o Proper credit must always be given to sources of information.

o Homework: Students may work together solving homework problems, but outright copying is not permitted.

o Show your work

o Academic dishonesty will be reported to the UIC judicial office.

· Attend , be on time, and come prepared for class

· Turn off cell phones and pagers

· Communicate with the instructor! Ask questions and make comments- in or out of class

· Provide feedback (negative and positive) to the instructor

· Seek help as soon as possible when encountering obstacles to progress

· Stay ahead of lectures and contribute to the group progress in building models

BioE472SyllabusF2009[1].doc