Advanced Computational Neuroscience
Course infoSchedule
Course moduleNWI-NM085C
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Wiskunde, Natuur- en Sterrenkunde;
prof. dr. H.J. Kappen
Other course modules lecturer
prof. dr. H.J. Kappen
Other course modules lecturer
Contactperson for the course
prof. dr. H.J. Kappen
Other course modules lecturer
prof. dr. H.J. Kappen
Other course modules lecturer
prof. dr. P.H.E. Tiesinga
Other course modules lecturer
Academic year2021
KW3-KW4  (31/01/2022 to 31/08/2022)
Starting block
Course mode
Registration using OSIRISYes
Course open to students from other facultiesYes
Waiting listNo
Placement procedure-
After successful completion of the course, you will be able to understand the computational neuroscience literature, in terms of being able to implement and critically evaluate these models in terms of whether they address the underlying neuroscience problem, and write computer programs to implement a number of common computational models.
The course consists of two parts. One part is given by Kappen and consists of the following topics: stochastic neuron models and networks, deep networks and sparse coding, learning rules for classification, Boltzmann Machines and reinforcement learning. This part contains many computer excercises that are also the final examination.
The other part is taught by Tiesinga uses the tools of nonlinear dynamics, neural networks and information theory (covered in Computational Neuroscience, NWI-NM047D) to study models for cognitive processes, such as attention, decision making, memory and learning. Each year there will be a variety of topics covered in the lectures choosen from amongst modern literature on reinforcement learning, neuromorphic computing (?),neural oscillations in cognitive processes and information transfer, plasticity, reduction of large-scale models to low-dimensional dynamics, and single neuron models at various levels of complexity. The students will do small problem sets in a weekly practice hour during the course (30%) and a final project (70%) for the end grade.

Presumed foreknowledge
Neurophysics 1 and Neurophysics 2
Test information
Written examination with practical exercises during the course, culminating in an end project.
The course relies on active student participation. The students will present part of the material. The examination is comprised of written and exam and is based as well on presentations during the course, the regular and computer assignments, and on an essay that summarizes the recent developments on a particular neuroscience topic.
Required materials
Reader for the Tiesinga part will be made available in at cost on bright space.
Dayan and Abbott, Theoretical Neuroscience, Computational and Mathematical Modeling of Neural Systems, MIT Press, paperback version, (2005) is required

Instructional modes
Course occurrence


Practical computer training

Final Grade
Test weight1
Test typeExam
OpportunitiesBlock KW4, Block KW4