{"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/using-stigmergy-to-incorporate-the-time-into","title":"Using stigmergy to incorporate the time into artificial neural networks","arxiv_id":"1811.10574","date":"2018-10-26","proceeding":null,"authors":["Galatolo Federico A.","Cimino Mario G. C. A.","Vaglini Gigliola"],"abstract":"A current research trend in neurocomputing involves the design of novel\nartificial neural networks incorporating the concept of time into their\noperating model. In this paper, a novel architecture that employs stigmergy is\nproposed. Computational stigmergy is used to dynamically increase (or decrease)\nthe strength of a connection, or the activation level, of an artificial neuron\nwhen stimulated (or released). This study lays down a basic framework for the\nderivation of a stigmergic NN with a related training algorithm. To show its\npotential, some pilot experiments have been reported. The XOR problem is solved\nby using only one single stigmergic neuron with one input and one output. A\nstatic NN, a stigmergic NN, a recurrent NN and a long short-term memory NN have\nbeen trained to solve the MNIST digits recognition benchmark.","url_abs":"http://arxiv.org/abs/1811.10574v1","url_pdf":"http://arxiv.org/pdf/1811.10574v1.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":"using-stigmergy-to-incorporate-the-time-into","repo_url":"https://github.com/galatolofederico/mike2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}