{"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/detecting-synapse-location-and-connectivity","title":"Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets","arxiv_id":"1807.02739","date":"2018-07-08","proceeding":null,"authors":["Toufiq Parag","Daniel Berger","Lee Kamentsky","Benedikt Staffler","Donglai Wei","Moritz Helmstaedter","Jeff W. Lichtman","Hanspeter Pfister"],"abstract":"Synaptic connectivity detection is a critical task for neural reconstruction\nfrom Electron Microscopy (EM) data. Most of the existing algorithms for synapse\ndetection do not identify the cleft location and direction of connectivity\nsimultaneously. The few methods that computes direction along with contact\nlocation have only been demonstrated to work on either dyadic (most common in\nvertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on\nboth types. In this paper, we present an algorithm to automatically predict the\nlocation as well as the direction of both dyadic and polyadic synapses. The\nproposed algorithm first generates candidate synaptic connections from\nvoxelwise predictions of signed proximity generated by a 3D U-net. A second 3D\nCNN then prunes the set of candidates to produce the final detection of cleft\nand connectivity orientation. Experimental results demonstrate that the\nproposed method outperforms the existing methods for determining synapses in\nboth rodent and fruit fly brain.","url_abs":"http://arxiv.org/abs/1807.02739v2","url_pdf":"http://arxiv.org/pdf/1807.02739v2.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":"detecting-synapse-location-and-connectivity","repo_url":"https://github.com/paragt/EMSynConn","is_official":1,"mentioned_in_paper":1,"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}