After successful completion of the course

• the student is able to calculate the response of a neuron or of a network of neurons to various inputs, both analytically and by computer simulations
• the student should be able to apply basic principles from Information Theory and Non-linear Systems analysis to quantify information processing by networks of neurons and to determine the attraction domain and stable states of a network of neurons.
• the student should be able to discuss the functional role (if any) of oscillations, neuronal correlations and neuronal variability based the homework problems and assigned literature 
  

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.