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Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization

Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing deep learning applications. In recent years, there have been several proposals focused on supervised (conversion, spike-based gradient descent) and unsupervised (spike timing dependent plasticity) training meth...

詳細記述

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書誌詳細
出版年:Front Neurosci
主要な著者: Panda, Priyadarshini, Aketi, Sai Aparna, Roy, Kaushik
フォーマット: Artigo
言語:Inglês
出版事項: Frontiers Media S.A. 2020
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7339963/
https://ncbi.nlm.nih.gov/pubmed/32694977
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnins.2020.00653
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