Neuromorphic computingΒΆ

Neuromorphic computing falls in one of my sweet spots. From a theoretical point of view, one of the key questions is to understand the types of computations that can be carried out in dynamical systems, and how the restrictions in architecture affect our ability to implement algorithms. Then there is the problem of how to best implement such systems.

While a lot of focus has been on the brain, my research is heavily inspired by the central neural system, and the whole integration of sensing, computing, and execution using neural architectures. This can have a tremendous impact for instance in the area of advanced sensors and robotics.

The approach that I have taken in this area is to step back from the materials and instead focus on understanding the dynamics of these systems from a mathematical point of view. This includes, for instance, exploring the difference between spiking and non-spiking systems in terms of their computational ability, and the impact of the network structure on the performance of the system.

The aim of this exploration is to identify the simplest possible architecture that can execute a given task and the develop the materials required to meet those requirements if they are not currently available.

A summary of our approach on this topic can be found here.