{"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/high-resolution-neural-connectivity-from","title":"High resolution neural connectivity from incomplete tracing data using nonnegative spline regression","arxiv_id":"1605.08031","date":"2016-05-24","proceeding":"NeurIPS 2016 12","authors":["Kameron Decker Harris","Stefan Mihalas","Eric Shea-Brown"],"abstract":"Whole-brain neural connectivity data are now available from viral tracing\nexperiments, which reveal the connections between a source injection site and\nelsewhere in the brain. These hold the promise of revealing spatial patterns of\nconnectivity throughout the mammalian brain. To achieve this goal, we seek to\nfit a weighted, nonnegative adjacency matrix among 100 $\\mu$m brain \"voxels\"\nusing viral tracer data. Despite a multi-year experimental effort, injections\nprovide incomplete coverage, and the number of voxels in our data is orders of\nmagnitude larger than the number of injections, making the problem severely\nunderdetermined. Furthermore, projection data are missing within the injection\nsite because local connections there are not separable from the injection\nsignal.\n  We use a novel machine-learning algorithm to meet these challenges and\ndevelop a spatially explicit, voxel-scale connectivity map of the mouse visual\nsystem. Our method combines three features: a matrix completion loss for\nmissing data, a smoothing spline penalty to regularize the problem, and\n(optionally) a low rank factorization. We demonstrate the consistency of our\nestimator using synthetic data and then apply it to newly available Allen Mouse\nBrain Connectivity Atlas data for the visual system. Our algorithm is\nsignificantly more predictive than current state of the art approaches which\nassume regions to be homogeneous. We demonstrate the efficacy of a low rank\nversion on visual cortex data and discuss the possibility of extending this to\na whole-brain connectivity matrix at the voxel scale.","url_abs":"http://arxiv.org/abs/1605.08031v3","url_pdf":"http://arxiv.org/pdf/1605.08031v3.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":"high-resolution-neural-connectivity-from","repo_url":"https://github.com/kharris/high-res-connectivity-nips-2016","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"matrix-completion","task_name":"Matrix Completion"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}