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Brain-Inspired Learning on Neuromorphic Substrates
[article]
2020
arXiv
pre-print
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the promise for scalable, low-power information processing on temporal data streams. Yet, to solve real-world problems, these networks need to be trained. However, training on neuromorphic substrates creates significant challenges due to the offline character and the required non-local computations of gradient-based learning algorithms. This article provides a mathematical framework for the design of practical
arXiv:2010.11931v1
fatcat:e7bwgrmynvgmfkuordiqb3zusq