Meta-learning from a dual machine-learning and biological perspective

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Learning in deep networks remains a significant challenge for both machinelearning and neuroscience. On the ML side, training artificial neural networks is generally slow,requires millions of training examples, and often relies on extensive hyper-parameteroptimization. On the neuroscience side, brains appear to navigate all these problemsalmost insolently -- yet the neural mechanisms that underpin learning have remainedelusive. It is unclear whether and how artificial deep learning algorithmscould be implemented in brain circuits. This project will unify ML and neuro approaches tolearning by studying meta-learning, i.e., the learning of learning strategies that (i)overcome the limitations of off-the-shelf algorithms and (ii) draw closer to the type ofbiophysically feasible solutions which brains use. The work will consist of mathematicaltheory and numerical simulations.