Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks.

ABSTRACT: Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. However, biases introduced by the apparatus, environment, or experimentalist represent a critical challenge to the test validity. We have recently developed the Modified Barnes Maze (MBM) task, a spatial learning paradigm that overcomes inherent behavioral biases of animals in the classical Barnes maze. The specific combination of spatial strategies employed by mice is often considered representative of the level of cognitive resources used. Herein, we have developed a convolutional neural network-based classifier of exploration strategies in the MBM that can effectively provide researchers with enhanced insights into cognitive traits in mice. F

SUBMITTER: Illouz T 

PROVIDER: S-EPMC11237060 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature