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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 (Landhuis et al. Nature 2017, Lauer et al. Nature Methods 2022). 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). We are 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 brain function guided by deep learning principles. We focus on understanding how a given behavioural outcome ultimately leads to credit being assigned to trillions of synapses across multiple brain areas – the credit assignment problem. To survive and adapt to dynamic and complex environments animals and humans must assign credit efficiently. Recently, we have shown that the brain can approximate deep learning algorithms (Sacramento et al. NeurIPS 2018, Blake et al. Nature Neuroscience 2019, Greedy et al. NeurIPS 2022, Boven et al. Nature Comms 2023). In this project, you will build on state-of-the-art computational models of AI-like credit assignment in the brain and contrast it with recent experimental observations at the behavioural, systems and circuit level.

Primary Supervisor


Research Group