You are here: Home / Teaching / Computational Neuroscience

Computational Neuroscience - Winter term 2022/23

A Lecture Series from Models to Applications

Interdisciplinary lecture series taught by neuroscience experts from TUM and LMU that provides an introduction to computational neuroscience. Topics range from a general overview on neurobiology and basic modeling to neuroengineering and -prothetics. In winter terms a focus is given to neuroengineering and -prothetics whereas summer terms cover topics more strongly related to biological mechanisms.

Some background on Julius Bernstein, who lent his name to the Bernstein Network: Julius Bernstein (1839–1917): pioneer neurobiologist and biophysicist.

For general inquiries on the lecture please get in touch with Dr. Kay Thurley.

Day and Time

Tuesday 18:00-19:30 s.t., winter term 2022/23

Venue

Munich Institute of Biomedical Engineering (MIBE), Technische Universität München, Boltzmannstr. 11, Hörsaal E.126 im Erdgeschoss
https://www.bioengineering.tum.de/ihr-weg-zu-uns

More information and lecture notes will be posted on our Moodle site Moodle@elearningTUM a couple of days before each lecture listed below.

Overview

No.DateLecturerTopic
1 10/18 Herz Modelling

Introduction to Computational Neuroscience

2 10/25 Luksch Biology

Neuroanatomy primer

11/01

no lecture -- All Saints' Day

3 11/08 Luksch Biology

Neurophysiology primer

4 11/15
5 11/22
6 11/29
7 12/06 Seeber Engineering

Neuroprosthetics I: Cochlear Implants: System overview and stimulation algorithms

8 12/13 Seeber Engineering

Neuroprosthetics II: Cochlear Implants: Electric stimulation of the auditory nerve, phenomenological models

12/20

no lecture

9 01/10
10 01/17
11 01/24 Sirota Integration

Methods of systems neuroscience: measurement and perturbation of neural activity

12 01/31 Sirota Integration

Systems mechanisms of learning and memory from theory to experimental data

Exam

Exam/Credits

3 ECTS
In the written examination, an overview of the various aspects of computational neuroscience will be tested. Knowledge-based learning outcomes from the lecture as well as the understanding and ability to solve (practical) problems will be assessed in a 60 min written examination with questions set and corrected by the respective lecturers. For questions on the exam please get in touch with Dr. Kay Thurley.
Here you can find an example exam and the sample solutions.


Previous editions