Content:

Computational neuroscience uses quantitative methods to describe what nervous systems do, study how they function, and explain the underlying principles. This seminar will cover recent scientific publications in the field of computational neuroscience. Specific topics:

• Neural Encoding

• Neural Decoding

• Information Theory

• The Action Potential

• Dynamics of Neural Networks

• Synaptic Plasticity

• Associative Networks

• Continuous Attractor Networks

• Associative Learning

• Classification

• Competitive Learning

• Generative Models

Learning Outcomes:

After successful completion of this seminar, students will be able to

• read and understand scientific articles in computational neuroscience

• apply computational models to describe the functioning of the nervous system

• understand the advantages and disadvantages of specific computational models

• discuss how neuroscience experiments are used to test computational models

• present the results of studies in computational neuroscience to an audience

Examination: 

Oral presentation

Prof. Dr. Sen Cheng Lecturer

(+49) 234-32-29486 sen.cheng@rub.de NB 3/33

Course type

Seminars

Credits

3

Term

Summer Term 2024

Knowledge of calculus, linear algebra, and statistics are required, e.g. Mathematik 1 und 2, Statistik. Knowledge of biology is not necessary, but basic computational neuroscience is.

Students should have taken the class „Introduction to Computational Neuroscience“, or something equivalent, before enrolling in this seminar. It is also possible to take this seminar in parallel with „Introduction to Computational Neuroscience“.

Literature:

The articles will be announced in the first meeting.
Background reading: “Theoretical Neuroscience” by Dayan and Abbott, MIT Press