Abstract
Complex Systems Science and Brain Dynamics: A Frontiers in Computational Neuroscience Special Topic
Highlights
Complexity explains the emergent behavior of interacting particles and is a theoretical approach inspired by physics, which has become a basis for computational simulations
Dynamical systems consist of equations governing the temporal evolution of values of interacting sets of variables and parameters (Alligood et al, 1997)
We have recently employed complex systems science to study the loss of synchronicity in the biological clock following travel (Leise and Siegelmann, 2006)
Summary
Complexity explains the emergent behavior of interacting particles and is a theoretical approach inspired by physics, which has become a basis for computational simulations. We have recently employed complex systems science to study the loss of synchronicity in the biological clock following travel (Leise and Siegelmann, 2006). We found that the chief source of de-synchronicity occurs when some organs advance their clocks following the SCN update, a property termed “anti-dromic re-entrainment.” Based on this model, we were able to suggest applications to avoid organs advancing their clocks in opposite directions, and avoid hard cases of jet-lag.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
