{"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-probabilistic-spiking-neural","title":"Training Probabilistic Spiking Neural Networks with First-to-spike Decoding","arxiv_id":"1710.10704","date":"2017-10-29","proceeding":null,"authors":["Alireza Bagheri","Osvaldo Simeone","Bipin Rajendran"],"abstract":"Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at\nharnessing the energy efficiency of spike-domain processing by building on\ncomputing elements that operate on, and exchange, spikes. In this paper, the\nproblem of training a two-layer SNN is studied for the purpose of\nclassification, under a Generalized Linear Model (GLM) probabilistic neural\nmodel that was previously considered within the computational neuroscience\nliterature. Conventional classification rules for SNNs operate offline based on\nthe number of output spikes at each output neuron. In contrast, a novel\ntraining method is proposed here for a first-to-spike decoding rule, whereby\nthe SNN can perform an early classification decision once spike firing is\ndetected at an output neuron. Numerical results bring insights into the optimal\nparameter selection for the GLM neuron and on the accuracy-complexity trade-off\nperformance of conventional and first-to-spike decoding.","url_abs":"http://arxiv.org/abs/1710.10704v3","url_pdf":"http://arxiv.org/pdf/1710.10704v3.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-probabilistic-spiking-neural","repo_url":"https://github.com/LucaMozzo/SpikingNeuralNetwork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"early-classification","task_name":"Early  Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.10704","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}