CERN Colloquium

Phase transitions in complex systems: Shaping information flow in neural networks and changing spreading dynamics of SARS-CoV-2

by Dr Viola Priesemann

Remote only (CERN)

Remote only



Biological as well as artificial networks show amazing information processing properties. We use approaches from statistical physics and information theory to uncover their operation principles and derive optimal design for a given task. A popular hypothesis is that neural networks profit from operating close to a continuous phase transition, because at a phase transitions, several computational properties are maximized. We show that maximizing these properties is advantageous for some tasks – but not for others. We then show how tuning networks away or towards a phase transition enables to adapt them to requirements. Thereby we shed light on the operation of biological neural networks, and inform the design of artificial ones. – In a second part of the talk, we address the spread of SARS-CoV-2 in Germany. We quantify how governmental policies and the concurrent behavioral changes led to a transition from exponential growth to decline of novel case numbers. We conclude with discussing potential scenarios of the SARS-CoV-2 dynamics for the months to come.

Cramer et al., Nature Communications, in press
Dehing et al., Science, in press
Wilting & Priesemann, Nature Communications, 2018
Zierenberg, Witling & Priesemann, PRX 2018

Password: 595682


Organized by

Wolfgang Lerche / TH-SP