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Updated: Mar 18, 2026
Barnes Maze Testing Strategies with Small and Large Rodent Models
Published on: February 26, 2014
Unbiased classification of spatial strategies in the Barnes maze
Tomer Illouz1, Ravit Madar2, Charlotte Clague1
1The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.
Motivation:
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.
Results:
Here, we offer a support vector machine-based, automated, Barnes-maze unbiased strategy (BUNS) classification algorithm, as well as a cognitive score scale that can be used for memory acquisition, reversal training and probe trials. The BUNS algorithm can greatly benefit Barnes maze users as it provides a standardized method of strategy classification and cognitive scoring scale, which cannot be derived from typical Barnes maze data analysis.
Availability And Implementation:
Freely available on the web at http://okunlab.wix.com/okunlab as a MATLAB application.
Contact:
eitan.okun@biu.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.
