{"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/linking-structure-and-activity-in-nonlinear","title":"Linking structure and activity in nonlinear spiking networks","arxiv_id":"1610.03828","date":"2020-02-25","proceeding":null,"authors":[],"abstract":"Recent experimental advances are producing an avalanche of data on both\nneural connectivity and neural activity. To take full advantage of these two\nemerging datasets we need a framework that links them, revealing how collective\nneural activity arises from the structure of neural connectivity and intrinsic\nneural dynamics. This problem of {\\it structure-driven activity} has drawn\nmajor interest in computational neuroscience. Existing methods for relating\nactivity and architecture in spiking networks rely on linearizing activity\naround a central operating point and thus fail to capture the nonlinear\nresponses of individual neurons that are the hallmark of neural information\nprocessing. Here, we overcome this limitation and present a new relationship\nbetween connectivity and activity in networks of nonlinear spiking neurons by\ndeveloping a diagrammatic fluctuation expansion based on statistical field\ntheory. We explicitly show how recurrent network structure produces pairwise\nand higher-order correlated activity, and how nonlinearities impact the\nnetworks' spiking activity. Our findings open new avenues to investigating how\nsingle-neuron nonlinearities---including those of different cell\ntypes---combine with connectivity to shape population activity and function.","url_abs":"http://arxiv.org/abs/1610.03828v3","url_pdf":"http://arxiv.org/pdf/1610.03828v3.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":"linking-structure-and-activity-in-nonlinear","repo_url":"https://github.com/gocker/PoissonGLMCumulants","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}