Math 490N/Biol 595N: Introduction to Computational Neuroscience

1
Math 490N/Biol 595NIntroduction to
Computational Neuroscience

• Course Organization
• Introduction
• Mathematical Models

2
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

3
Different Kind of Course

• First time course offeredan experiment
• We in the course have very different kinds of
  backgrounds
• Our backgrounds do not prepare us for the course
  material
• Instructor doesnt know much about the subject

4
Organization

• Math 490N vs Biol 595N
• Work in groups
• Homework
• Report on paper from the literature
• Midterm and Final Exam
• Academic adjustments

5
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

6
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

7
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

8
Mathematical Models

• Life is one big story problem!

9
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

10
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