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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