AI-driven brain-wide computational models of learning.
We are at an exciting turning point in neuroscience. New technologies now allow us to measure and control neural activity and behaviour with unprecedented detail. At the same time, new theoretical frameworks are starting to reveal how rich behaviours arise from synaptic, circuit and systems computations (Richards et al. Nature Neuroscience 2019). Our group is contributing directly to the latter by aiming to understand how we learn. To this end, we are developing a new generation of computational models of learning driven by recent machine learning developments.
We focus on understanding how a given behavioural outcome ultimately leads to credit being assigned to trillions of synapses across multiple brain areas – credit assignment problem. To this end and in order to have a unified understanding of how we learn to produce adaptable behaviours it is important to jointly study the contribution of three different systems: (i) cortical circuits, (ii) neuromodulation and (iii) subcortical regions.
You can find more details on our external website.
We co-organise the Oxford NeuroTheory Forum.
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We are actively recruiting PhDs and postdocs.