{"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/deep-learning-in-spiking-neural-networks","title":"Deep Learning in Spiking Neural Networks","arxiv_id":"1804.08150","date":"2018-04-22","proceeding":null,"authors":["Amirhossein Tavanaei","Masoud Ghodrati","Saeed Reza Kheradpisheh","Timothee Masquelier","Anthony S. Maida"],"abstract":"In recent years, deep learning has been a revolution in the field of machine\nlearning, for computer vision in particular. In this approach, a deep\n(multilayer) artificial neural network (ANN) is trained in a supervised manner\nusing backpropagation. Huge amounts of labeled examples are required, but the\nresulting classification accuracy is truly impressive, sometimes outperforming\nhumans. Neurons in an ANN are characterized by a single, static,\ncontinuous-valued activation. Yet biological neurons use discrete spikes to\ncompute and transmit information, and the spike times, in addition to the spike\nrates, matter. Spiking neural networks (SNNs) are thus more biologically\nrealistic than ANNs, and arguably the only viable option if one wants to\nunderstand how the brain computes. SNNs are also more hardware friendly and\nenergy-efficient than ANNs, and are thus appealing for technology, especially\nfor portable devices. However, training deep SNNs remains a challenge. Spiking\nneurons' transfer function is usually non-differentiable, which prevents using\nbackpropagation. Here we review recent supervised and unsupervised methods to\ntrain deep SNNs, and compare them in terms of accuracy, but also computational\ncost and hardware friendliness. The emerging picture is that SNNs still lag\nbehind ANNs in terms of accuracy, but the gap is decreasing, and can even\nvanish on some tasks, while the SNNs typically require much fewer operations.","url_abs":"http://arxiv.org/abs/1804.08150v4","url_pdf":"http://arxiv.org/pdf/1804.08150v4.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":"deep-learning-in-spiking-neural-networks","repo_url":"https://github.com/pereirarodrigo/project_spike","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-learning-in-spiking-neural-networks","repo_url":"https://github.com/pereirarodrigo/spike","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08150","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}