{"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/semiparametric-spectral-modeling-of-the","title":"Semiparametric spectral modeling of the Drosophila connectome","arxiv_id":"1705.03297","date":"2017-05-09","proceeding":null,"authors":["Carey E. Priebe","Youngser Park","Minh Tang","Avanti Athreya","Vince Lyzinski","Joshua T. Vogelstein","Yichen Qin","Ben Cocanougher","Katharina Eichler","Marta Zlatic","Albert Cardona"],"abstract":"We present semiparametric spectral modeling of the complete larval Drosophila\nmushroom body connectome. Motivated by a thorough exploratory data analysis of\nthe network via Gaussian mixture modeling (GMM) in the adjacency spectral\nembedding (ASE) representation space, we introduce the latent structure model\n(LSM) for network modeling and inference. LSM is a generalization of the\nstochastic block model (SBM) and a special case of the random dot product graph\n(RDPG) latent position model, and is amenable to semiparametric GMM in the ASE\nrepresentation space. The resulting connectome code derived via semiparametric\nGMM composed with ASE captures latent connectome structure and elucidates\nbiologically relevant neuronal properties.","url_abs":"http://arxiv.org/abs/1705.03297v1","url_pdf":"http://arxiv.org/pdf/1705.03297v1.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":"semiparametric-spectral-modeling-of-the","repo_url":"https://github.com/youngser/mbstructure","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Position"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}