Computational properties of network dynamical systems

Dynamical systems concepts have mostly been developed to understand the behaviour of autonomous, i.e. input-free, nonlinear systems. Even in this case, it is well recognized that such systems can display a wide range of dynamical behaviours. Understanding how non-autonomous systems behave is an additional mathematical challenge that gives insight into how complex systems can perform computational activities in response to inputs. In this talk I will discuss ways that the dynamics of network attractors can be used to describe and predict not only the how the systems perform computations, but also how they may make errors during the computations.

Speaker Biography

Peter Ashwin is Professor of Mathematics at the University of Exeter (UK) since 2007. His main interests are in nonlinear dynamical systems and applications: bifurcation theory and dynamical systems, especially synchronization problems, symmetric chaotic dynamics, spatially extended systems and nonautonomous systems. Applications of dynamical systems include climate (bifurcations, tipping points), fluids (bifurcations and mixing), laser systems (synchronization), neural systems (computational properties), materials and electronic systems (digital signal processing) and biophysical modelling (cell biology).