{"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/lemonade-learned-motif-and-neuronal-assembly","title":"LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos","arxiv_id":"1806.09963","date":"2018-06-26","proceeding":"ICLR 2019 5","authors":["Elke Kirschbaum","Manuel Haußmann","Steffen Wolf","Hannah Jakobi","Justus Schneider","Shehabeldin Elzoheiry","Oliver Kann","Daniel Durstewitz","Fred A. Hamprecht"],"abstract":"Neuronal assemblies, loosely defined as subsets of neurons with reoccurring\nspatio-temporally coordinated activation patterns, or \"motifs\", are thought to\nbe building blocks of neural representations and information processing. We\nhere propose LeMoNADe, a new exploratory data analysis method that facilitates\nhunting for motifs in calcium imaging videos, the dominant microscopic\nfunctional imaging modality in neurophysiology. Our nonparametric method\nextracts motifs directly from videos, bypassing the difficult intermediate step\nof spike extraction. Our technique augments variational autoencoders with a\ndiscrete stochastic node, and we show in detail how a differentiable\nreparametrization and relaxation can be used. An evaluation on simulated data,\nwith available ground truth, reveals excellent quantitative performance. In\nreal video data acquired from brain slices, with no ground truth available,\nLeMoNADe uncovers nontrivial candidate motifs that can help generate hypotheses\nfor more focused biological investigation.","url_abs":"http://arxiv.org/abs/1806.09963v1","url_pdf":"http://arxiv.org/pdf/1806.09963v1.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":"lemonade-learned-motif-and-neuronal-assembly","repo_url":"https://github.com/EKirschbaum/LeMoNADe","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","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}