Gehring, T., Luksys, G., Sandi, C. et al. (1 more author) (2015) Detailed classification of swimming paths in the Morris Water Maze: multiple strategies within one trial. Scientific Reports, 5. 14562 . ISSN 2045-2322
Abstract
The Morris Water Maze is a widely used task in studies of spatial learning with rodents. Classical performance measures of animals in the Morris Water Maze include the escape latency, and the cumulative distance to the platform. Other methods focus on classifying trajectory patterns to stereotypical classes representing different animal strategies. However, these approaches typically consider trajectories as a whole, and as a consequence they assign one full trajectory to one class, whereas animals often switch between these strategies, and their corresponding classes, within a single trial. To this end, we take a different approach: we look for segments of diverse animal behaviour within one trial and employ a semi-automated classification method for identifying the various strategies exhibited by the animals within a trial. Our method allows us to reveal significant and systematic differences in the exploration strategies of two animal groups (stressed, non-stressed), that would be unobserved by earlier methods.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Behavioural methods; Machine learning; Software |
Dates: |
|
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: | 18 Apr 2016 10:30 |
Last Modified: | 18 Apr 2016 10:30 |
Published Version: | http://dx.doi.org/10.1038/srep14562 |
Status: | Published |
Publisher: | Nature Publishing Group |
Refereed: | Yes |
Identification Number: | 10.1038/srep14562 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:96372 |