Mathematical Neuroscience Tutorials
September 2002 – August 2003

Past Tutorials:

Tutorial on neurophysiology and neural encoding

Organizers: John Miller, Alex Dimitrov. Topics Include: information theory and analysis

of massive multi-spike data streams.

This two-day tutorial will introduce postdocs and interested faculty and students to the study of neural coding. The workshop on neural coding will take place during February 10-14. On the first day, John Miller will focus on neurophysiological and experimental issues. On the second day, Alex Dimitrov will focus on theoretical and analytical issues, and will summarize techniques from information theory and multivariate statistics that have been productive in recent studies of neural coding. As well as providing background for the workshops, the "tutors" will define some of the major conceptual and technical barriers to our understanding of neural computation and representation, and present a few recently developed approaches that show promise for getting around these barriers. The tutors will also "hang out" with any interested MBI attendees in the afternoon hours following the tutorial lectures, to address any questions that may arise during the lectures, and to design some effective supplemental tutorials to be given by other visiting workshop participants during the afternoon hours of workshop 3.

Thursday, January 9: John Miller: Tutorial lectures on neurophysiology and neural encoding: 9:00-10:00, 10:30-12:00. Discussion and "office hours": 2:00-5:00.
Friday, January 10: Alex Dimitrov: Tutorial lectures on statistical analysis of neural data: 9:00-10:00, 10:30-12:00. Discussion and "office hours": 2:00-5:00.

The tutors have identified two new books that would be of considerable benefit to workshop participants, and which will be discussed at the tutorials and at the workshop. Note that the authors of the second book below will be at the workshop. The books are as follows:

1. Arathorn, D. (2002). Map-seeking circuits in visual cognition: A computational mechanism for biological and machine vision. Stanford, CA: Stanford University Press. ISBN: 0-8047-4277-4.

2. Eliasmith, C., & Anderson, C.H. (2002). Neural Engineering: Computation, representation, and dynamics in neurobiological systems. Cambridge, MA: MIT Press. ISBN: 0-262-05071-4.

John Miller and Alex Dimitrov will be posting additional suggested references over the period leading up to the tutorials.

August 26-30
A Crash Course: Introduction to Biology (by Brian Smith) and Introduction to Dynamical Systems (by David Terman)

The lectures will take place at Caldwell Lab 177 (near the Math Tower).

8/26 Monday:	Brian lectures 9-10am, 10:30-11:30am; David mentors 2-3pm
8/27 Tuesday:	Brian lectures 9-10am, 10:30-11:30am; David mentors 2-3pm
8/28 Wednesday:	Brian lectures 9-10am, 10:30-11:30am; David lectures 2-3pm
8/29 Thursday:	David lectures 9-10am, 10:30-11:30am; David mentors 2-3pm
8/30 Friday:	David lectures 9-10am, 10:30-11:30am; David mentors 2-3pm

The course will introduce the postdocs and other interested students and faculty to basic concepts in biology (cells, neurons, etc.) and to elementary concepts and tools in dynamical systems.

Copies of tansparencies/lecture notes will be made available during the course.

 

Autumn Quarter 2002
Neuronal Systems

The program will begin with a tutorial introducing post-docs and interested faculty to neuronal dynamics at all levels: from the sub-cellular to the network, as well as system level modeling.

Tutorial on Neuronal Dynamics (September 5-12):
John Rinzel, September 5-6, 9:00-10:00, 10:30-11:30am
David Terman, September 9-10, 9:00-10:00, 10:30-11:30am
Bard Ermentrout, September 11-12, 9:00-10:00, 10:30-11:30am

The lectures will take place at Caldwell Lab 177 (near the Math Tower).

The tutorial will consider modeling, analytic and computatinal aspects of neuronal systems. We will discuss the Hodgkin-Huxley model for electrical activity in a nerve axon, simple models for chemical synapses, and properties of larger networks. Mathematical topics include coupled oscillators, averaging, geometric singular perturbation theory, synchronization and other types of pattern formation such as propagating waves and bursting oscillations. Finally, we will demonstrate how numerical software such as XPPAUT can be used to analyze the neuronal models.