{"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/supervised-learning-based-on-temporal-coding","title":"Supervised learning based on temporal coding in spiking neural networks","arxiv_id":"1606.08165","date":"2016-06-27","proceeding":null,"authors":["Hesham Mostafa"],"abstract":"Gradient descent training techniques are remarkably successful in training\nanalog-valued artificial neural networks (ANNs). Such training techniques,\nhowever, do not transfer easily to spiking networks due to the spike generation\nhard non-linearity and the discrete nature of spike communication. We show that\nin a feedforward spiking network that uses a temporal coding scheme where\ninformation is encoded in spike times instead of spike rates, the network\ninput-output relation is differentiable almost everywhere. Moreover, this\nrelation is piece-wise linear after a transformation of variables. Methods for\ntraining ANNs thus carry directly to the training of such spiking networks as\nwe show when training on the permutation invariant MNIST task. In contrast to\nrate-based spiking networks that are often used to approximate the behavior of\nANNs, the networks we present spike much more sparsely and their behavior can\nnot be directly approximated by conventional ANNs. Our results highlight a new\napproach for controlling the behavior of spiking networks with realistic\ntemporal dynamics, opening up the potential for using these networks to process\nspike patterns with complex temporal information.","url_abs":"http://arxiv.org/abs/1606.08165v2","url_pdf":"http://arxiv.org/pdf/1606.08165v2.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":"supervised-learning-based-on-temporal-coding","repo_url":"https://github.com/TianjianCai/SNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.08165","atlas_url":"https://app.syntology.ai/?focus=1606.08165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.08165"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/TianjianCai/SNN","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"5d997d1e25178db8","entry":"loss_func","repo":"TianjianCai/SNN","repo_kind":"listed","path":"SNN.py","file_url":"https://github.com/TianjianCai/SNN/blob/HEAD/SNN.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5d997d1e25178db8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}