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However, direct SNN training algorithms are not well compatible with error back\u2010propagation process, while indirect conversion algorithms based on artificial neural networks (ANNs) are usually accuracy\u2013lossy due to various approximation errors. Both of them suffer from lower accuracies compared with their reference ANNs and need lots of time steps to achieve stable performance in deep architectures. In this article, a novel conversion framework is presented for deep SNNs with negative\u2010spike dynamics, which takes a quantization constraint and spike compensation technique into consideration during ANN\u2010to\u2010SNN conversion, and a truly lossless accuracy performance with their ANN counterparts is obtained. The converted SNNs can retain full advantages of simple leaky\u2010integrate\u2010and\u2010fire spiking neurons and are very suited for hardware implementation. In the experimental results, it is shown that converted spiking LeNet on MNIST\/FashionMNIST and VGG\u2010Net on CIFAR\u201010 dataset yield the state\u2010of\u2010the\u2010art classification accuracies with quite shortened computing time steps and much fewer synaptic operations.<\/jats:p>","DOI":"10.1002\/aisy.202300383","type":"journal-article","created":{"date-parts":[[2023,10,22]],"date-time":"2023-10-22T23:46:39Z","timestamp":1698018399000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Toward a Lossless Conversion for Spiking Neural Networks with Negative\u2010Spike Dynamics"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2571-3213","authenticated-orcid":false,"given":"Chenglong","family":"Zou","sequence":"first","affiliation":[{"name":"School of Mathematical Science Peking University  Beijing 100871 China"},{"name":"Peking University Chongqing Research Institute of Big Data  Chongqing 400030 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxin","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits Peking University  Beijing 100871 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits Peking University  Beijing 100871 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits Peking University  Beijing 100871 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4951-4286","authenticated-orcid":false,"given":"Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits Peking University  Beijing 100871 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,10,22]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"K.Simonyan A.Zisserman (Preprint) arXiv:1409.1556 [cs.CV] submitted: Sept. 2014 https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_10_4_1","unstructured":"C. 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