Back to overviewTime and date: Mon 22 June 2020 11:00
Abstract: Biological as well as artificial networks show amazing information processing properties. 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 homeostatic plasticity enables us to tune networks away or towards a phase transition, and thereby adapt the network to task requirements. Thereby we shed light on the operation of biological neural networks, and inform the design and self-organization 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.