I am interested in supervising students with a strong mathematical, computational or neuroscience background. Projects could be carried out in several possible areas relating to the work in the group. Some suggestions for topics that would be interesting to me are below, but I'm very happy to consider other possibilities. In addition to working within the group, studying at Imperial College provides excellent opportunities for interacting with other theoretical and experimental researchers, both at Imperial (recently ranked 8th in the world in the QS world university rankings) and in the many neuroscience groups in London.
Applicants for a PhD position should initially send me a brief CV and cover letter with a description of research interests or a proposed project, and will eventually have to formally apply through the standard Imperial College mechanism (for more information, see here). There are several opportunities for funding which we can discuss if you are offered a position. Note that a masters degree is required for PhD study at Imperial: please see the PhD requirements page (and the Country-specific requirements).
I am also interested in supervising PhD students in neuroinformatics as part of the HiPEDS CDT (Centre for Doctoral Training in High Performance Embedded and Distributed Systems). Note that a masters degree is also required for entry.
I do not currently have any open postdoctoral positions, but please get in touch if you are interested in applying for your own funding through a fellowship scheme, for example.
- Theory of spiking neurons. How does the brain compute using spikes? Does this represent a new paradigm of computation that is substantially different from existing notions of digital and analogue computation? If so, what can it do better and how? Specific questions I'm interested in are: can spiking cell assemblies or populations represent information in a fundamentally different way to single spiking neurons or assemblies of rate-based neurons? Can spiking neurons be used to multiplex computations to solve combinatorial explosion problems? Can sub-networks dynamically reconfigure themselves for different tasks and usefully communicate amongst themselves despite this dynamic reconfiguration? Can adaptation and inhibition be used to extract useful signals in the presence of noise?
- Auditory neuroscience: I'm particularly interested in how the brain processes realistic auditory environments, where sound comes in a continuous stream, interesting signals can be both expected or unexpected, there can be multiple sound sources, background noise, etc. This could involve computational modelling or technological applications.
- Spiking neural network simulation. For example, running simulations on non-standard computational hardware, such as field-programmable gate arrays (FPGAs), graphics processing units (GPUs) and the SpiNNaker spiking neural network supercomputer.
- Analysing large scale neural data. New experimental techniques are becoming available which provide several orders of magnitude more data than were previously available, but there is not yet agreement on methods for using this data to understand how the brain functions.