About this course
The module presents a variety of fundamental models and methods from computational neuroscience. By solving daily exercises the students learn how to practically apply the acquired concepts. The course introduces the employed more
advanced mathematical tools embedded into the different topics. Further there will be a pre-course teaching the required programming skills in python.
Learning outcomes
Dynamical systems in neuroscience:
- linear algebra, matrices and vectors, linear differential equations
- linear stability concept
- rate models in neuroscience
- synaptic plasticity and learning
Spiking models
- binary neurons
- a model for associative memory: Hopfield networks
- leaky integrate-and-fire neurons
- the balanced state of cortical networks
Cognitive modeling
- probability measures, integrals, distributions
- instantaneous decision models from economics & psychology
- dynamic decision models: drift-diffusion models, decision field theory
Classification with neurons
- representational similarity analysis
- pattern classification analysis
- support vector machines
- deep learning
Prerequisites for participation
None
Necessary language skill
English
Semester(s) in which the module takes place
Summer Semester
Course type
Lecture + Practical + Seminar
Course level
Masters
Course start date
13 May 2024
Course end date
14 Jun 2024
Involvement period
4 weeks
Teaching mode
Local
Local attendance
Required
Workload in ECTS
7.5
Graded
Yes
Person in charge of module
Prof. Dr. Tatjana Tchumachenko, Prof. Dr. Raoul-Martin Memmesheimer, Prof. Dr. Dominik Bach, Prof. Dr. Lukas Kunz
Local module ID
WPM 32