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

深度网络中的学习仍然是机器学习和神经科学的重大挑战。在机器学习方面，训练人工神经网络通常很慢，需要数百万个训练示例，并且通常依赖于广泛的超参数优化。从神经科学的角度来看，大脑似乎可以毫无顾忌地驾驭所有这些问题，然而支撑学习的神经机制却仍然难以捉摸。目前还不清楚人工深度学习算法是否以及如何在大脑电路中实现。该项目将通过研究元学习，即，学习策略的学习，（i）克服现成算法的局限性，（ii）更接近大脑使用的生物物理学可行的解决方案。这项工作将包括电磁理论和数值模拟。

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.