— Computational Neuroscience
Mathematical models and data analysis techniques used to describe computations performed by nervous systems. Lecture topics include single-neuron biophysics, neural coding, network dynamics, memory, plasticity, and learning. Lab topics include programming mathematical models and data analysis techniques in MATLAB.
Topics
- single-neuron biophysics
- neural coding
- network dynamics
- memory
- plasticity
- and learning
Topics explicitly listed in official catalogue description
Prerequisites & course information
Course Description: Mathematical models and data analysis techniques used to describe computations performed by nervous systems. Lecture topics include single-neuron biophysics, neural coding, network dynamics, memory, plasticity, and learning. Lab topics include programming mathematical models and data analysis techniques in MATLAB. Learning Activities: Lecture 4 hour(s), Lecture/Lab 3 hour(s). Cross Listing: NSC 267 . Grade Mode: Letter. Learning Activities: Lecture 4 hour(s), Lecture/Lab 3 hour(s). Cross Listing: NSC 267 . Grade Mode: Letter.
Sources
- Course catalogue ↗2026-2027