We study both machine learning systems and biological brains: learning from the brain how to advance technology, and vice versa use advanced technology to reverse-engineer the brain – we call it “Neurotechnology”.

Our research fuses neuroscience with technology contributing to the emerging discipline of neurotechnology. We combine methods from computing, physics and engineering with experimental human studies to understand how the brain works: We pursue both basic science and translational work by a. reverse engineering from first principles the algorithms that drive brains and behaviour and b. translating this understanding into technology that helps patients and people in general.

Our research questions are centred on a basic characteristic of biological systems: noise, uncertainty or variability in behaviour. Variability can be observed across many levels of biological behaviour: from the movements of our limbs, the responses of neurons in our brain, to the interaction of biomolecules. Such variability is emerging as a key ingredient in understanding biological principles (Faisal, Selen & Wolpert, 2008, Nature Rev Neurosci) and yet lacks adequate quantitative and computational methods for description and analysis. Crucially, we find that biological and behavioural variability contains important information that our brain and our technology can make us of (instead of just averaging it away): The brain knows about variability and uncertainty and it is linked to its own computations. Therefore, we use and develop statistical machine learning techniques, to predict behaviour and analyse data.

Our cross-disciplinary outlook is reflected by us being part of the Dept. of Bioengineering & Dept. of Computing (South Kensington Campus) and the MRC Clinical Sciences Centre (Hammersmith Hospital Campus). Our team and lab facilities are housed centrally inside Bioengineering at the Royal School of Mines Building on Prince Consort Rd (strategically next to the Natural History Museum and Hyde Park)


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