- The student acquires knowledge about the physiology and dynamical behavior of neurons and neural networks in both health and disease.
- The student understands key concepts from nonlinear dynamics, including equilibrium, limit cycles, stability and synchronization.
- The student is able to describe various aspects of the dynamic behavior with mathematical tools in relation to abnormal network function as present in e.g. epilepsy, ischemia and Parkinson's disease.
- The student is able to perform simulations to assist in a better understanding of physiology and pathology, including normal and abnormal EEG rhythms.
Complex networks of interacting neurons define the physiological properties of various brain functions, including motor control, language, perception, autonomic control, and memory. Intuitive reasoning about these networks is often sufficient for some global understanding or guiding a particular treatment or experiment. A more profound understanding, however, will provide tools to contribute to new developments and insights, relevant for improved diagnostics and innovative therapeutic approaches.|
In the 2nd year course (Neural System), various issues related to membrane and network dynamics were touched upon. The emphasis, however, was rather conceptual, and various issues were treated at the introductory level. This course will present a much more in-depth treatment of physiology and dynamic behaviour of neurons and neural networks. From the clinical perspective, this will be integrated in relation with various diseases of the central or peripheral nervous system and the EEG.
We will discuss basic concepts from nonlinear dynamics, including equilibrium, limit cycles, homoclinic orbits, stability and synchronization. Modeling and simulation will be introduced to better understand individual behavior of neurons and their interactions.
Various examples from basis neuroscience to clinical neurology and neurophysiology will be treated, with an emphasis on epilepsy, ischaemia and neuromodulation (e.g. for the treatment of seizures or movement disorders). The course also discusses how modeling and simulation can contribute to a better understanding of normal and pathological EEG rhythms.
Assumed previous knowledge
|A Bachelor Degree in one of the following disciplines:Technical Medicine, Applied Phsics, Biomedical Engineering, Applied Mathematics, Electrical Engineering, Advanced Technology.The TM Master course 'Biological control systems' is strongly recommended.|
TG Ba students could follow the course (as extra-curricular course) when they have completed the courses: Neural Systems, Signal Analysis, the Statistics part of the two epidemiology courses in the Bachelor and have ample experience with Matlab
|Master Technical Medicine|
|Master Applied Mathematics|
|Master Biomedical Engineering|
|Master Electrical Engineering||Required materials|
|Lecture Notes and hand-outs|
|Eugene M. Izhikevich. Dynamical systems in Neuroscience: The geometry of Excitability and Bursting. The MIT Press, 2007|
RemarkExam (70%) + Group Assignment (30%)
Both results have to be >5.5