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Lookup NU author(s): Jennifer Tulip, Jonas Zimmermann, Professor Andrew Jackson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2017 The Authors Background Behavioural training through positive reinforcement techniques is a well-recognised refinement to laboratory animal welfare. Behavioural neuroscience research requires subjects to be trained to perform repetitions of specific behaviours for food/fluid reward. Some animals fail to perform at a sufficient level, limiting the amount of data that can be collected and increasing the number of animals required for each study. New method We have implemented automated positive reinforcement training systems (comprising a button press task with variable levels of difficulty using LED cues and a fluid reward) at the breeding facility and research facility, to compare performance across these different settings, to pre-screen animals for selection and refine training protocols. Results Animals learned 1- and 4-choice button tasks within weeks of home enclosure training, with some inter-individual differences. High performance levels (∼200–300 trials per 60 min session at ∼80% correct) were obtained without food or fluid restriction. Moreover, training quickly transferred to a laboratory version of the task. Animals that acquired the task at the breeding facility subsequently performed better both in early home enclosure sessions upon arrival at the research facility, and also in laboratory sessions. Comparison with existing method(s) Automated systems at the breeding facility may be used to pre-screen animals for suitability for behavioural neuroscience research. In combination with conventional training, both the breeding and research facility systems facilitate acquisition and transference of learning. Conclusions Automated systems have the potential to refine training protocols and minimise requirements for food/fluid control.
Author(s): Tulip J, Zimmermann JB, Farningham D, Jackson A
Publication type: Article
Publication status: Published
Journal: Journal of Neuroscience Methods
Year: 2017
Volume: 285
Pages: 6-18
Print publication date: 15/06/2017
Online publication date: 01/05/2017
Acceptance date: 28/04/2017
Date deposited: 31/05/2017
ISSN (print): 0165-0270
ISSN (electronic): 1872-678X
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.jneumeth.2017.04.015
DOI: 10.1016/j.jneumeth.2017.04.015
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