Course Information for 5005/6005 - Computational Neuroscience
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Primary Instructor: Chuck Alan Dorval
Credits: 3.0
Semesters Offered:Spring
Frequency Offered:Odd Years
Prerequisites:Open to graduate students having taken traditional undergraduate level: Signals Analysis, Linear Algebra, and Differential Equations, and some programming experience, preferably in Matlab or Octave.
Class Schedule: MW 10:45-12:15
Catalog Description: This course explores advances in neuroscience enabled by progress in computational power. This course reviews the quantitative underpinnings of classical neurophysiological understanding, but the focus is using modern computational techniques to broaden that understanding. Topics include: simulating biophysical models of neuronal membranes; constructing computational neuronal models and deconstructing them with techniques from nonlinear dynamics; models of neural plasticity and adaptive behavior, and their interface with various learning algorithms; and neural information encoding and algorithms for neural decoding of informational quantity and representational content. Students are expected to perform a substantial amount of programming in problem sets and a course project.
Required Texts:(1) G. Bard Ermentrout & David H. Terman, Mathematical Foundations of Neuroscience (Interdisciplinary Applied Mathematics, Vol. 35), ISBN: 978-0387877075. (2) Peter Dayan & L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, ISBN:978-0262541855.
Optional Texts:(3) Christof Koch, Biophysics of Computation : Information Processing in Single Neurons, ISBN:978-0195181999. (4) Thomas Fischer Weiss, Cellular Biophysics: Transport & Electrical Properties, Vols. 1-2, ISBN:978-0262231831 & ISBN:978-0262231848. (5) Eugene M. Izhikevich, Dynamical Systems in Neuroscience: the Geometry of Excitability and Bursting. ISBN:978-0262514200. (6) Fred Rieke, David Warland, Rob de Ruyter van Steveninck & William Bialek, Spikes: Exploring the Neural Code. ISBN: 978-0262681087.
Course Syllabus:Syllabus_5005/6005.pdf