{"id":13992622,"url":"https://github.com/benlansdell/rdd","last_synced_at":"2025-07-22T16:31:02.734Z","repository":{"id":80718430,"uuid":"103695196","full_name":"benlansdell/rdd","owner":"benlansdell","description":"python code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper","archived":false,"fork":false,"pushed_at":"2023-04-06T03:12:44.000Z","size":29380,"stargazers_count":9,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-11-29T13:39:53.916Z","etag":null,"topics":["causal-inference","computational-neuroscience","economics","leaky-integrate-and-fire-model","neural-network"],"latest_commit_sha":null,"homepage":"https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011005","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/benlansdell.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2017-09-15T19:45:22.000Z","updated_at":"2023-06-14T13:49:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"fd7387e2-e59d-430f-ad25-db9c791caafb","html_url":"https://github.com/benlansdell/rdd","commit_stats":null,"previous_names":[],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/benlansdell/rdd","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benlansdell%2Frdd","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benlansdell%2Frdd/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benlansdell%2Frdd/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benlansdell%2Frdd/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/benlansdell","download_url":"https://codeload.github.com/benlansdell/rdd/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/benlansdell%2Frdd/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266530876,"owners_count":23943999,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-22T02:00:09.085Z","response_time":66,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causal-inference","computational-neuroscience","economics","leaky-integrate-and-fire-model","neural-network"],"created_at":"2024-08-09T14:02:04.103Z","updated_at":"2025-07-22T16:31:00.223Z","avatar_url":"https://github.com/benlansdell.png","language":"Jupyter Notebook","readme":"## Neural spiking for causal inference and learning\n\nBen Lansdell and Konrad Kording 2023\n\n**Abstract:** When a neuron is driven beyond its threshold, it spikes. The fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent-based learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. We show how spiking enables neurons to solve causal estimation problems and that local plasticity can approximate gradient descent using spike discontinuity learning.\n\n`python` code and jupyter notebooks to reproduce figures from our PLOS Computational Biology paper ([here](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011005)).\n\nSee the text for more details.\n\n![alt text](assets/fig1.png \"Figure 1\")\n","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenlansdell%2Frdd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenlansdell%2Frdd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenlansdell%2Frdd/lists"}