1/13/20041 Math 490N/Biol 595N: Introduction to Computational Neuroscience Course Organization Introduction Mathematical Models

1/13/20042 Goals of the Course Experience working in a multi-disciplinary team of scientists Increase tolerance to cognitive discomfort in learning/working situation Learn basics of neurophysiology, differential equations, dynamical systems, and some related computer tools Become familiar with some classical models of neural systems

1/13/20043 Different Kind of Course First time course offered…an experiment We in the course have very different kinds of backgrounds Our backgrounds do not prepare us for the course material Instructor doesn’t know much about the subject

1/13/20044 Organization Math 490N vs Biol 595N Work in groups Homework Report on paper from the literature Midterm and Final Exam Academic adjustments

1/13/20045 Who are we? Name Course: Math 490N, Biol 595N, or “audit” Status at Purdue: “junior”, “1st yr grad”, “postdoc” Scientific background/major College level biology courses taken College level math courses taken Other interesting information

1/13/20046 Introduction Rita Colwell (NSF): “We're not near the fulfillment of biotechnology's promise. We're just on the cusp of it…” 19th Century Biology: descriptive 20th Century Biology: biochemical 21st Century Biology: quantitative/mathematical Eric Lander (Whitehead Inst): “The 21st Century Biologist must be, at least in part, a mathematician.” NSF and NIH are concerned that there are not enough people trained to join hands across the disciplinary boundary between biology and math

1/13/20047 Why? Flooded with data -- need some way to organize it! Efficiency: mathematical models can do “virtual experiments” faster, more cheaply, and in more difficult conditions than in a wet lab. Simplifications: mathematics can hide the complexity of a situation behind an organizing concept

1/13/20048 Mathematical Models Life is one big story problem!

1/13/20049 Mathematical Models Life is one big story problem! Create a mathematical description of experimental data that can be used to extend, interpolate, or manipulate the data

1/13/ Mathematical Models Life is one big story problem! Create a mathematical description of experimental data that can be used to extend, interpolate, or manipulate the data A simple example: Population model