I am interested in foundations of neural networks and models of computation in the brain. I have an interest in spiking neural networks (SNN). SNNs are a (potentially) energy efficient alternative to deep neural networks. The problem is that this energy efficiency can only be achieved if the networks are run on neuromorphic hardware, which in turn limits the learning algorithms that can be used. Most importantly, backpropagation-based methods do not run well on neuromorphic hardware. My research is concerned with finding alternative learning methods that perform equally well as backpropagation, but are friendly to neuromorphic hardware.
My other research interest is stochastic modelling. I am particularly interested in biological systems as natural information processors. The ability of biochemical networks to compute seems to be limited by the stochastic fluctuations these systems. Overcoming fluctuation usually comes at the cost of higher energy dissipation. For some biological systems it is also possible to define a "speed" of computation. It is then possible to formulate a speed-cost-accuracy trade-off of the "biological computer."
I am also interested in the limits to using science to solve societal problems. In the public (and indeed scientific) discourse the "scientific method" is often equated with "rationality." I believe that this view is naive and have expressed my thoughts on this in my popular science book The Science Myth: God, society, the self and what we will never know .
My research publications can be found here. Beside my research papers, I published together with David Barnes a text book Guide to Simulation and Modeling for Biosciences to help students make their first steps in biological modelling.
If you would like to do a PhD in a topic related to my research interest, please contact me to discuss possible projects. There is funding available for students from the UK. There is general information available on this page.
21/10/2023