{"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/full-force-a-target-based-method-for-training","title":"full-FORCE: A Target-Based Method for Training Recurrent Networks","arxiv_id":"1710.03070","date":"2017-10-09","proceeding":null,"authors":["Brian DePasquale","Christopher J. Cueva","Kanaka Rajan","G. Sean Escola","L. F. Abbott"],"abstract":"Trained recurrent networks are powerful tools for modeling dynamic neural\ncomputations. We present a target-based method for modifying the full\nconnectivity matrix of a recurrent network to train it to perform tasks\ninvolving temporally complex input/output transformations. The method\nintroduces a second network during training to provide suitable \"target\"\ndynamics useful for performing the task. Because it exploits the full recurrent\nconnectivity, the method produces networks that perform tasks with fewer\nneurons and greater noise robustness than traditional least-squares (FORCE)\napproaches. In addition, we show how introducing additional input signals into\nthe target-generating network, which act as task hints, greatly extends the\nrange of tasks that can be learned and provides control over the complexity and\nnature of the dynamics of the trained, task-performing network.","url_abs":"http://arxiv.org/abs/1710.03070v1","url_pdf":"http://arxiv.org/pdf/1710.03070v1.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":"full-force-a-target-based-method-for-training","repo_url":"https://github.com/briandepasquale/full-FORCE-demos","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":"https://app.syntology.ai/?focus=1710.03070","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.03070"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/briandepasquale/full-FORCE-demos","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"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":"3e4dd9222c18152e","entry":"create_parameters","repo":"briandepasquale/full-FORCE-demos","repo_kind":"listed","path":"Python/FF_Demo.py","file_url":"https://github.com/briandepasquale/full-FORCE-demos/blob/HEAD/Python/FF_Demo.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3e4dd9222c18152e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}