WS 2026/2027 Computational Neuroscience: Models of Neural Systems Richard Kempter and Benjamin Lindner

Dates: Weekly, from 19-October-2026 to 08-February-2027.

Location: Bernstein Center for Computational Neurosciences Berlin, Haus 6, Philippstr. 13.

Times:

• Theoretical Lectures 2112132 (2 SWS, 2 ECTS):
  Mondays from 10:00h to 11:30h (BCCN lecture hall, Haus 6);
  first lecture on October 19, 2026.
  
• Analytic Tutorials 2112446 (2 SWS, 4 ECTS):
  Thursdays, 16-18h (location: Haus 4, seminar room);
  first brief introdution on October 22nd, 2026, 17:00-17:30h
  
• Experimental Lectures 2112690 (2 SWS, 2 ECTS):
  Mondays from 16:15h to 17:45h (BCCN lecture hall, Haus 6);
  first lecture on October 19, 2026.
  
• Computer Practical 2112133 (2 SWS, 4 ECTS):
  Thursdays from 14-16h (ITB seminar room 012a, Haus 4);
  Additional, extended introduction to Python (if needed): Tuesday, October 20, 16-20h and Thursday October 22nd, 14-17h (ITB seminar room 012a, Haus 4);
  

Target Group: Students of Computational Neuroscience, Medical Neuroscience, Biology, Biophysics, Physics, Mathematics, Computer Science, and Psycholgy.

Here is the full description of the master modules Models of Neural Systems (Master CNS, TU) and P24.3.e (Master Physics, HU).

Requirements: Basic knowledge in Mathematics (e.g. calculus, differential equations, algebra) and a higher programming language (e.g. C, C++, Python, MatLab).

Aims and Topics: Participants should learn basic concepts, their theoretical foundation, and the common models used in Computational Neuroscience. The Module ''Models of Neural Systems'' also provides some neurobiological knowledge and explains the relevant theoretical approaches as well as the findings resulting form these approaches so far. After completing the Module, participants should understand strengths and limitations of the different models. Participating students will learn to appropriately choose the theoretical methods for modeling cellular neural systems. They will learn how to apply these methods while taking into account the neurobiological findings, and they should be able to critically evaluate results obtained. Participants should also be able to adapt models to new problems as well as to develop new models of neural systems.

Moodle Pages and People:

• Theoretical Lectures by Richard Kempter and Benjamin Lindner
  
• Analytic Tutorials by Ikhwan Bin Khalid and Atilla Botond Kelemen and Arne Großelindemann and Jakob Stubenrauch.
  
• Experimental Lectures organized by Lisa Velenosi and Carolin Gehr.
  
• Computer Practical by Stefano Masserini and Archil Sakevarashvili + Rike-Benjamin Schuppner (for the introduction to Python)
  

Overview about the contents of the module:
Further details are available at this page.

Analytic Tutorials: To receive a course certificate ("Schein") certifying successful participation, you must obtain 50% of the total points across six short written assessments (''minitests'', see below for details on grading, content and goal). You can practice for the minitests with the weekly problem sets (exercise sheets).

Computer Practicals: To receive a course certificate ("Schein") certifying successful participation, two criteria must be fulfilled:
1) Three short written assessments (''minitests'', see below) must be passed, i.e., on average at least 50% of the points must be obtained. You can practice for the minitests with the weekly problem sets (exercise sheets).
2) In a final project phase, students replicate parts of recent scientific papers and summarize their results in a short report and an oral presentation on Feb-8-2027.

Minitests and problem sets (same structure for analytic tutorials and computer practicals):
- Passing minitests: at least 50% of the total minitest points must be obtained separately for the analytic tutorials (6 minitests) and for the computer practicals (3 minitests).
- Minitest content: A minitest is a short (15-20 min) hand-written assessment taken in person. The minitests contain simplified versions of selected problems similar to the weekly problem sets. To practice, we encourage students to solve these problem sets by themselves (i.e. without using AI).
- Four advantages of solving the weekly problem sets:
1) Practice: To practice for the minitests, you may opt to turn in solutions to the weekly problem sets electronically (ideally as handwritten solutions for the analytic tutorials), in groups of 2-3 people. We encourage you to do this every week.
2) Feedback: Students submitting their solutions (in groups of 2-3 people) will obtain written feedback from the tutors. We cannot guarantee feedback for single-person submissions.
3) General discussion: Solutions to the problem sets are discussed in the tutorials.
4) Presenting solutions for bonus points: Students can earn up to 2.5% points per successful presentation of a problem solution in the weekly tutorials, up to a maximum of 5% points per semester. These points count towards the 50% total required to pass the minitests. For example, earning the full 5% bonus points reduces the required minitest points to 45%.

Module Examinations: Oral exam (graded); certificates of successful participation in the tutorials and/or practicals is a prerequisite for the oral exam. The exam days are March 10th, 11th, and 12th, 2027. To prepare for the exams, there will a special meeting of students and examiners on Monday, Feb-8-2027 at 11:45h.

Recommended reading:

P. Dayan and L.F. Abbott (2001) Theoretical Neuroscience. MIT Press, Cambridge, Massachusetts.

E. M. Izhikevich (2007) Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. MIT Press, Cambridge, Massachusetts.

Johnston, Wu (1995) Foundations in Cellular Neurophysiology, MIT Press, Cambrigdge, Massachusetts.

Advanced/additional reading:

M. F. Bear, B. W. Connors, M. A. Paradiso (2007) Neuroscience: Exploring the Brain, Lippincott Williams & Wilkins, Baltimore, Maryland.

Thomas P. Trappenberg (2002) Fundamentals of Computational Neuroscience. Oxford University Press, Oxford, UK.

P. Churchland and T. Sejnowski (1994). The Computational Brain. MIT Press, Cambridge, MA.