A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

Abstract

The Morris Water Maze is commonly used in behavioural neuroscience for the study of spatial learning with rodents. Over the years, various methods of analysing rodent data collected during this task have been proposed. These methods span from classical performance measurements to more sophisticated categorisation techniques which classify the animal swimming path into behavioural classes known as exploration strategies. Classification techniques provide additional insight into the different types of animal behaviours but still only a limited number of studies utilise them. This is primarily because they depend highly on machine learning knowledge. We have previously demonstrated that the animals implement various strategies and that classifying entire trajectories can lead to the loss of important information. In this work, we have developed a generalised and robust classification methodology to boost classification performance and nullify the need for manual tuning. We have also made available an open-source software based on this methodology.

Metadata

Item Type:	Article
Authors/Creators:	Vouros, A. https://orcid.org/0000-0002-3383-6133 Gehring, T.V. Szydlowska, K. Janusz, A. Tu, Z. Croucher, M. Lukasiuk, K. https://orcid.org/0000-0002-1582-0920 Konopka, W. https://orcid.org/0000-0002-4994-2459 Sandi, C. https://orcid.org/0000-0001-7713-8321 Vasilaki, E. https://orcid.org/0000-0003-3705-7070
Copyright, Publisher and Additional Information:	© 2018 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Swimming Path; Morris Water Maze (MWM); Boosting Classification; Machine Learning Knowledge; Segmentation Length
Dates:	Accepted: 28 September 2018 Published (online): 10 October 2018 Published: 10 October 2018
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	22 Oct 2018 11:22
Last Modified:	08 May 2024 13:38
Status:	Published
Publisher:	Nature Research
Refereed:	Yes
Identification Number:	10.1038/s41598-018-33456-1
Related URLs:	PubMed URL Dataset
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:137432

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A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

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A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

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