{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/training-spiking-deep-networks-for","title":"Training Spiking Deep Networks for Neuromorphic Hardware","arxiv_id":"1611.05141","date":"2016-11-16","proceeding":null,"authors":["Eric Hunsberger","Chris Eliasmith"],"abstract":"We describe a method to train spiking deep networks that can be run using\nleaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for\nspiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012\nbenchmark. Our method for transforming deep artificial neural networks into\nspiking networks is scalable and works with a wide range of neural\nnonlinearities. We achieve these results by softening the neural response\nfunction, such that its derivative remains bounded, and by training the network\nwith noise to provide robustness against the variability introduced by spikes.\nOur analysis shows that implementations of these networks on neuromorphic\nhardware will be many times more power-efficient than the equivalent\nnon-spiking networks on traditional hardware.","url_abs":"http://arxiv.org/abs/1611.05141v1","url_pdf":"http://arxiv.org/pdf/1611.05141v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"training-spiking-deep-networks-for","repo_url":"https://github.com/akrizhevsky/cuda-convnet2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1611.05141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.05141"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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