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  1. 1. 1/13/2004 1 Math 490N/Biol 595N:Math 490N/Biol 595N: Introduction to ComputationalIntroduction to Computational NeuroscienceNeuroscience Course Organization Introduction Mathematical Models
  2. 2. 1/13/2004 2 Goals of the CourseGoals 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. 3. 1/13/2004 3 Different Kind of CourseDifferent 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
  4. 4. 1/13/2004 4 OrganizationOrganization  Math 490N vs Biol 595N  Work in groups  Homework  Report on paper from the literature  Midterm and Final Exam  Academic adjustments
  5. 5. 1/13/2004 5 Who are we?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. 6. 1/13/2004 6 IntroductionIntroduction  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. 7. 1/13/2004 7 Why?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. 8. 1/13/2004 8 Mathematical ModelsMathematical Models  Life is one big story problem!
  9. 9. 1/13/2004 9 Mathematical ModelsMathematical 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. 10. 1/13/2004 10 Mathematical ModelsMathematical 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

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