z-logo
open-access-imgOpen Access
Unbiased classification of spatial strategies in the Barnes maze
Author(s) -
Tomer Illouz,
Ravit Madar,
Charlotte Clague,
Kathleen J. Griffioen,
Yoram Louzoun,
Eitan Okun
Publication year - 2016
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btw376
Subject(s) - morris water navigation task , barnes maze , computer science , artificial intelligence , machine learning , categorization , spatial learning , cognition , support vector machine , water maze , psychology , neuroscience , hippocampus
Spatial learning is one of the most widely studied cognitive domains in neuroscience. The Morris water maze and the Barnes maze are the most commonly used techniques to assess spatial learning and memory in rodents. Despite the fact that these tasks are well-validated paradigms for testing spatial learning abilities, manual categorization of performance into behavioral strategies is subject to individual interpretation, and thus to bias. We have previously described an unbiased machine-learning algorithm to classify spatial strategies in the Morris water maze.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom