Winter Term 2019-20 / Neural Inf Process


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 GUIDELINES  •  MODULE HANDBOOK 2019-20  •  IMPORTANT DATES  •  WEEK PLAN  •  EXAM SCHEDULE

Course title

Theoretical Methods for Computational Neuroscience I (Elective)

Lecturer
Giese
Credits
1.5
Course content / topics

Objective:
Computational neuroscience builds partly on advanced theoretical concepts from multiple fields, including mathematics, physics, control theory, machine learning, and signal processing. The course consists of a series of tutorials on such advanced topics combined with in-class exercises. The course runs over two semesters (total: 3 credits) with about 7 sessions per semester. The topics complement the material covered in the main lectures Neural Dynamics and Machine Learning.

Course Schedule & Topics (the exact dates will be negotiated in the first class on Oct 29, 8 am, GTC Lecture Hall)

Learning targets:
Participants will learn basic methods for the analysis of electrical circuits and their frequency behavior with applications to neural structures, some basics from electrical network theory and frequency analysis. In addition, advanced integral transformations are discussed, as well as basics of variational calculus.

Prerequisites:
The course complements specifically the Lecture Neural Dynamics, where some basic concepts cannot be treated in depth. The benefit will be optimal for students who also attend this course, especially if they do not have background in physics or electrical engineering.  

Specific literature will be provided during the lecture.

Please note: Theoretical Methods for Computational Neuroscience I and II can be taken independently, each with 1.5 credits.

Day, time & location

Tue, 08 - 10 am, GTC Lecture Hall