Content |
The aim of this course is to give a theoretical description of the neuronal dynamics at the level of a single neuron and at the population level. The theoretical model will be used to explain the information processing and the storage and retrieval of information by populations of neurons. The course consists of two parts. One part is given by Kappen and consists of the following topics: Integrate and fire neurons, networks of binary neurons, synaptic plasticity, supervised and unsupervised learning, classical conditioning, reinforcement learning, control theory. The other part is taught by Tiesinga and is comprised of the following topics: nonlinear dynamics of neurons and systems of neurons, population coding in combination with Fisher information, information theory, neural-mass models and models and function of oscillations. |
Additional comments |
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. |
Topics |
For part Kappen, see http://www.snn.ru.nl/~bertk/comp_neurosci/ For the part Tiesinga, the course materials will be placed on blackboard |
Test information |
Written examination with practical exercises during the course, culminating in an end project. |
Prerequisites |
Neurophysics 1 and Neurophysics 2 |
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