Self-organization, criticality and optimality in neuronal network models and the brain

01.12.2017 bis 30.11.2022

The human brain is an amazing computational device. It can perceive, process, and store great amounts of information using its 80 billion neurons, each of them
interacting with thousands of other neurons. Only recently, experimental techniques have been developed for sampling the activity of hundreds of neurons in
parallel, offering unprecedented access to the collective dynamics. To make use of these rich data and understand the capabilities of a system of such complexity, theoretical understanding is essential. To this end, statistical physics provides unique tools to deal with systems of many interacting units. I propose to extend them to characterize the collective dynamics of spiking neural networks and investigate the interplay between dynamics, topology, and information processing
capabilities of the network.