Stochastic models in biology 1000-1L13MSB

Biological systems such as neural networks process and store information in their activities or structure. Empirical observations suggest that this information processing is very efficient despite of molecular noise (stochasticity). It means that neurons in the brain can very accurately encode the incoming information from the outside world, and write it in synapses in a stable manner, which is related to learning and memory. Learning processes are also present on a macro scale in the creation of moving spatial structures (swarms, clusters of cooperators in spatial evolution games).
The proseminar will be devoted to mathematical modeling of these topics, using probabilistic models with the elements of information theory and machine learning. We do not assume any detailed biological knowledge of students - all the necessary facts will be given during initial lectures (meetings). Also, the necessary mathematical tools will be presented in the course of the seminar.
Mathematical requirements: knowledge of the basics of probability theory, and the ability to analyze and solve simple ordinary differential equations.