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Computational Neuroscience - Summer term 2024

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., summer term 2024


LMU Main Building, Geschwister-Scholl-Platz 1, E 006
floor plan


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


1 04/16 Herz Modelling

Introduction to Computational Neuroscience


no lecture

2 04/30 Luksch Biology

Neuroanatomy primer

3 05/07 Luksch Biology

Neurophysiology primer

4 05/14 Młynarski Modelling

Information theory in neurobiology


no lecture -- Pentecost
5 05/28


Modelling Probabilistic inference in the brain
6 06/04 Gjorgjieva Modelling

Plasticity and development of neural circuits

7 06/11 Busse Integration

Visual system I: neurobiology

8 06/18


Modelling Visual system II: computation
9 06/25


Engineering Neuroprosthetics
10 07/02 Sirota Integration

Methods of systems neuroscience: measurement and perturbation of neural activity

11 07/09 Sirota Integration

 Systems mechanisms of learning and memory from theory to experimental data

12 07/16 Thurley Integration

Temporal cognition

07/24 Exam

Time: Wednesday, July 24, 11 a.m.
Venue: TUM, Raum N1189Theresienstr. 90, 80333 München

Registration for LMU students until July 16 by email to  Dr. Kay Thurley!


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.

Here you can find an example exam and the sample solutions. Due to the limited number of questions and the slightly changing lecture content and the lecturers, we cannot provide further example exams. Please ask the individual lectures for further help, e.g., self-assessment questions.

For general questions on the exam please get in touch with Dr. Kay Thurley.

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