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To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learningdoi:10.1073/pnas.2109194119 pmid:35042792 pmcid:PMC8794842 fatcat:j3x7ocfndjanrhxys6rbicqrgi