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Computational Neuroscience - Summer term 2022

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 2021/22


LMU Main Building, Geschwister-Scholl-Platz 1, E 004
floor plan

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


1 04/26 Luksch Biology

Motivation for doing Computational Neuroscience; Neuroanatomy primer

2 05/03 Luksch Biology

Neurophysiology primer

3 05/10 Herz Modelling

Single neuron models

4 05/17 Gjorgjieva Modelling

 Plasticity and development of neural circuits

5 05/24 Seeber Engineering


6 05/31 Busse Integration

Visual system I: neurobiology


no lecture -- Pentecost

7 06/14 Wachtler Modelling

 Visual system II: computation

8 06/21 Flanagin Integration

 Human neuroimaging (fMRI), Modeling connections between brain regions

9 06/28 Flanagin Integration

Spatial perception and navigation

10 07/05 Sirota Integration

Methods of systems neuroscience: measurement and perturbation of neural activity

11 07/12 Sirota Integration

 Systems mechanisms of learning and memory from theory to experimental data

12 07/19 Thurley Integration

Temporal cognition

08/02 Exam

Time: 11:15 a.m.
Venue: tba

Registration for LMU students until July 20 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. For questions on the exam please get in touch with Dr. Kay Thurley.
Here you can find an example exam and the sample solutions.

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