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Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware.
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Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, and Kaushik Roy · 2019
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Supervised learning based on temporal coding in spiking neural networks
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
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Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi · 2019
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Temporal coding in spiking neural networks with alpha synaptic function
Iulia M Comsa, Thomas Fischbacher, Krzysztof Potempa, Andrea Gesmundo, Luca Versari, and Jyrki Alakuijala · 2020
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Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
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Enabling spike-based backpropagation for training deep neural network architectures
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