{"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/learning-recurrent-dynamics-in-spiking","title":"Learning recurrent dynamics in spiking networks","arxiv_id":"1803.06622","date":"2018-03-18","proceeding":null,"authors":["Christopher Kim","Carson Chow"],"abstract":"Spiking activity of neurons engaged in learning and performing a task show\ncomplex spatiotemporal dynamics. While the output of recurrent network models\ncan learn to perform various tasks, the possible range of recurrent dynamics\nthat emerge after learning remains unknown. Here we show that modifying the\nrecurrent connectivity with a recursive least squares algorithm provides\nsufficient flexibility for synaptic and spiking rate dynamics of spiking\nnetworks to produce a wide range of spatiotemporal activity. We apply the\ntraining method to learn arbitrary firing patterns, stabilize irregular spiking\nactivity of a balanced network, and reproduce the heterogeneous spiking rate\npatterns of cortical neurons engaged in motor planning and movement. We\nidentify sufficient conditions for successful learning, characterize two types\nof learning errors, and assess the network capacity. Our findings show that\nsynaptically-coupled recurrent spiking networks possess a vast computational\ncapability that can support the diverse activity patterns in the brain.","url_abs":"http://arxiv.org/abs/1803.06622v2","url_pdf":"http://arxiv.org/pdf/1803.06622v2.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":"learning-recurrent-dynamics-in-spiking","repo_url":"https://github.com/chrismkkim/SpikeLearning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.06622","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}