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

Basic data

Self-organization, criticality and optimality in neuronal network models and the brain
12/1/2017 to 11/30/2022
Abstract / short description:
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.
neural networks
statitistical physics
information processing
mathematical methods

Involved staff


Department of Informatics
Faculty of Science
Werner Reichardt Center for Integrative Neuroscience (CIN)
Bernstein Center for Computational Neuroscience Tübingen (BCCN)
Interfaculty Institutes

Local organizational units

Institute for Theoretical Physics (ITP)
Department of Physics
Faculty of Science


Bonn, Nordrhein-Westfalen, Germany

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