[Colloq] Talk: Hava Siegelmann, 12/15, 12:00pm, 366 WVH - Dynamics of Multistage Circadian System and Applications to JetLag

[Biron, Jessica]

Hava Siegelmann, Professor, UMass Amherst

Talk: 12/15, 12:00pm, 366 WVH

Dynamics of Multistage Circadian System and Applications to JetLag

Abstract:
Sleep and wakefulness are only two of the physiological processes that run on a roughly 24-hour-cycle, or circadian clock. Tissues throughout the body exhibit circadian rhythms, forming a multi-oscillatory system whose disruption results in 'jet lag' and other health problems in travelers and rotational shift workers.  Our dynamics simulations of a multistage circadian system predict that jet lag tends to be most severe following an eastward change of five to eight time zones due to prolonged system desynchrony.  This desynchrony is due in part to differing reentrainment rates among components; our study however, points to a much greater source of desynchrony - "antidromic reentrainment." In this process, some peripheral tissues advance their relative phase shift by following the advancing SCN, while others delay their clock for a whole cycle until reaching synch. This revelation explains existing data previously considered "chaotic." Based on multistage system dynamics, we design a simple protocol that results in a more orderly transition avoiding antidromic reentrainment in all components - reducing reentrainment from nearly two weeks to just a few days for the most difficult shifts. Applications to shift work and sleep regularity are noted.

Bio:
Dr. Siegelmann is a professor of computer science at the University of Massachusetts Amherst. Her research focuses on mathematical and computational studies of the Brain, Intelligence and cognition, big data analytics and their industrial/biomedical applications. She is also a Director of the Biologically Inspired Neural and Dynamical Systems (BINDS) Lab and a core member of the Five College Neuroscience Program. Siegelmann is an internationally recognized expert in analog computation, neural networks, modeling biological systems and their disorders. She is the inventor of Super-Turing computation - a subfield connecting dynamical systems theory with computation, biology and physics. Her Support Vector Clustering algorithm (a joint work with Vladimir Vapnik) has been used extensively in industry for Big Data Analytics. Siegelmann's current supported research centers on adaptive memory vis-à-vis complex systems, AI and robotics - supported by the Office of Naval Research (ONR). Her other supported research focuses on the study of brain-like analog versus discrete processes and the construction of more brain-like analog hardware - underwritten by the NSF. Siegelmann is an editor of the Frontiers in Computational Neuroscience, Neural Networks journal, and the physics journal, Chaos; she was recently re-elected as a governor of the International Neural Networks Society.