{"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/a-generalised-framework-for-detailed","title":"A generalised framework for detailed classification of swimming paths inside the Morris Water Maze","arxiv_id":"1711.07446","date":"2017-11-20","proceeding":null,"authors":["Avgoustinos Vouros","Tiago V. Gehring","Kinga Szydlowska","Artur Janusz","Mike Croucher","Katarzyna Lukasiuk","Witold Konopka","Carmen Sandi","Zehai Tu","Eleni Vasilaki"],"abstract":"The Morris Water Maze is commonly used in behavioural neuroscience for the\nstudy of spatial learning with rodents. Over the years, various methods of\nanalysing rodent data collected in this task have been proposed. These methods\nspan from classical performance measurements (e.g. escape latency, rodent\nspeed, quadrant preference) to more sophisticated methods of categorisation\nwhich classify the animal swimming path into behavioural classes known as\nstrategies. Classification techniques provide additional insight in relation to\nthe actual animal behaviours but still only a limited amount of studies utilise\nthem mainly because they highly depend on machine learning knowledge. We have\npreviously demonstrated that the animals implement various strategies and by\nclassifying whole trajectories can lead to the loss of important information.\nIn this work, we developed a generalised and robust classification methodology\nwhich implements majority voting to boost the classification performance and\nsuccessfully nullify the need of manual tuning. Based on this framework, we\nbuilt a complete software, capable of performing the full analysis described in\nthis paper. The software provides an easy to use graphical user interface (GUI)\nthrough which users can enter their trajectory data, segment and label them and\nfinally generate reports and figures of the results.","url_abs":"http://arxiv.org/abs/1711.07446v2","url_pdf":"http://arxiv.org/pdf/1711.07446v2.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":"a-generalised-framework-for-detailed","repo_url":"https://github.com/RodentDataAnalytics/mwm-ml-gen","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"robust-classification","task_name":"Robust classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}