"The why and how of randomness", a càrrec de Susanne Ditlevsen (Department of Mathematical Sciences of the University of Copenhagen)

"The why and how of randomness" a càrrec de Susanne Ditlevsen (Department of Mathematical Sciences of the University of Copenhagen)

Susanne Ditlevsen is a professor at the Department of Mathematical Sciences of the University of Copenhagen, where she heads the section of statistics and probability theory. She completed her Ph.D. biostatistics in 2004 from Department of Biostatistics, University of Copenhagen. In 2012, she became an elected member of the International Statistical Institute, in 2016, to the Royal Danish Academy of Sciences and Letters and in 2018 to the Applied Mathematics Committee of the European Mathematical Society.

Her research focuses on the study mathematical models for biological processes, especially models that take into account randomness in the system, and she develops statistical methods for these models. She works especially with models for how the brain processes sensory input.

Dimecres 5 juny 2019 a les 12 h a la sala d'actes de l'FME. Activitat oberta a tota la comunitat matemàtica.  The talk will be accessible to a general audience.

"The why and how of randomness"

Abstract: Randomness is ubiquitous in all biological systems. The nervous system  is hugely affected by stochasticity, and noise and variability are fundamental of brain function. Information processing is faced with the challenge of acting in this unpredictable and random world, but even if cellular and molecular processes of life itself are noisy and variability in perception and behavior is observed for equal sensory inputs, the brain displays a remarkable precision, essential for survival. Is this ever-present noise a caprice of nature, which evolution has taught us to deal with the best we can? Or does stochasticity play a constructive role, increasing reliability and robustness, and the brain is a probabilistic device because this makes us more fit to survive? Is the noise beneficial or detrimental? Modeling with stochastic processes becomes more and more popular in neuroscience, not only because of the powerful mathematical tools from stochastic analysis, but also because of the increasing availability of measurements and data for dynamical processes, where randomness plays a major role.

In this talk I will discuss stochastic models of biological systems, and show some surprising and different dynamics not present in the deterministic models. The standard approach to deal with the noisy and highly variable data is to average over trials, to presumably get a more reliable output for further analysis. This might blur or entirely remove essential characteristics and mechanisms, which are fundamental for understanding the underlying function of the system under study. More advanced statistical methods and stochastic models are paramount to disentangle the finer mechanisms, because single trials carry information, which is not maintained in the average behavior.