Stochastic mechanisms, often summarized under “noise”, are ubiquitous in artificial and biological networks. In applications to genetic circuits the goal is to trace back the various manifestations of noise to a few sources on underlying levels like the stochastic fluctuations in biochemical reactions, to follow the propagation and modification of noise towards the cellular level and to pursue the interaction of different sources of noise with respect to their mutual repression or amplification. In applications to neural systems the focus is on microscopic noise, e.g. the thermal fluctuations on channel dynamics, the effect of synaptic noise on neuronal firing, but also on large-scale collective fluctuations of brain areas, information loss, effects of large-scale noise on attention and decision-making, as well as on bridges between the different levels. A comparison between both types of applications should reveal parallels in how natural systems transform or utilize noise. It is the tools of theoretical physics that provide a deeper understanding of its role, in particular when it acts counterintuitive. Advanced methods from statistical physics and nonlinear dynamics allow for predicting the action of noise on excitable media like neural systems, extinction events in various populations and noise-induced rare events.

We invite applications from graduate students, PhD students and postdocs with a background in theoretical physics, applied mathematics with an interest in biological applications and neuroscience. Applicants from the experimental side should be interested in the mathematical modeling and analysis of experimental data.

Registration NOISE (2)