After following this course, the student
1. has knowledge about the physiology and dynamical behaviour of neurons and neural networks in both health and disease.
2. can apply key concepts from nonlinear dynamics, including equilibrium, limit cycles, stability and synchronization.
3. is able to describe various aspects of the dynamic behaviour with mathematical tools in relation to abnormal network function as present in, e.g. epilepsy, ischemia and Parkinson's disease.
4. 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 a first global understanding or guiding a particular treatment or experiment. However, a more profound understanding will provide tools to contribute to new developments and insights relevant for improved diagnostics and innovative therapeutic approaches.
The second-year TG bachelor module "Neurale systeem en onderzoek" touches upon various issues related to membrane and network dynamics. The emphasis, however, was rather conceptual, and the treatise was mainly at an introductory level. This course will present a much more in-depth treatment of the physiology and dynamic behaviour of neurons and neural networks. From a clinical perspective, we will integrate this knowledge 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. Modelling and simulation will be introduced to understand the individual behaviour of neurons and their interactions better.
Various examples from basic 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 modelling and simulation can contribute to a better understanding of normal and pathological EEG rhythms.
Exam (50%) + Homework 30%)+ Group Assignment (20%)
Exam results need to be >5.5