PHYS366
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Computational Modeling of Neural Systems
Physics
College of Liberal Arts
Course Subject Code
PHYS
Course Number
366
Status
Active
Course Attributes
BINT: GenEd-Breadth/Interdisciplinar, CEA: ProgCLA-CEA and Au Pair, EMMT: Major-Mathematics Elective, EMPY: Major-Physics BA Elective, MMAT: Major-Mathematics, MPHY: Major-Physics BA, ENDA: Minor-Data Science Elective, ENMH: Minor-Mathematics Elective, NDAT: Minor-Data Science, NMAT: Minor-Mathematics
Course Short Title
Computational Mod of Neu Syst
Course Long Title
Computational Modeling of Neural Systems
Course Description
Computational neuroscience is the study of the brain as a computational and information-processing organ. It is a highly interdisciplinary field that employs various ideas and techniques from physics, biology, chemistry, mathematics, computer science, psychology, and (of course) neuroscience. In this course, we cover the following topics: biophysics of a single neuron; dynamics of neural networks; models of associative memory and object recognition; and numerical methods and tools for analyzing and simulating a dynamical system. We study the fundamental biophysical properties and processes of the neurons and their networks, while also learning to use several analytical and numerical methods for studying a complex dynamical system. The goal of the course is to develop an interdisciplinary approach for analyzing a biological system.
Min
4
Repeatable
-
Equivalent Course(s)
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