Abstract
Environmental enrichment (EE) paradigms are designed to enhance laboratory animals surroundings to encourage natural behaviors. Some enrichment paradigms also include a social component, based on the social interactions typical of the genus and species. Novel automatic methodologies based on image are becoming useful tools to improve laboratory works. This paper present a first approach to the automatic image analysis of laboratory rats in EE: behaviour, drug effects and pathology. The new methodology is based on image and Machine Learning paradigms and will become a useful tool for Neuroscience issues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baker, M.: Animal models: inside the minds of mice and men. Nature 475, 123–128 (2011). https://doi.org/10.1038/475123a. https://www.nature.com/articles/475123a
Eguiraun, H., López-de-Ipiña, K., Martinez, I.: Application of entropy and fractal dimension analyses to the pattern recognition of contaminated fish responses in aquaculture. Entropy 16(11), 6133–6151 (2014). https://doi.org/10.3390/e16116133. https://www.mdpi.com/1099-4300/16/11/6133
Rat genome sequencing project consortium: genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428(6982), 493–521 (2004). https://doi.org/10.1038/nature02426. https://www.nature.com/articles/nature02426
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948). https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Shannon, C.E.: Prediction and entropy of printed English. Bell Syst. Tech. J. 30(1), 50–64 (1951). https://doi.org/10.1002/j.1538-7305.1951.tb01366.x
Tecott, L.H., Nestler, E.J.: Neurobehavioral assessment in the information age. Nature Neuroscience 7(5), 462–466 (2004). https://doi.org/10.1038/nn1225. https://www.nature.com/articles/nn1225
Van Meer, P.J.K., Graham, M.L., Schuurman, H.J.: The safety, efficacy and regulatory triangle in drug development: impact for animal models and the use of animals. Eur. J. Pharmacol. 759, 3–13 (2015). https://doi.org/10.1016/j.ejphar.2015.02.055. http://www.sciencedirect.com/science/article/pii/S0014299915002551
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
López-de-Ipiña, K., Cepeda, H., Requejo, C., Fernandez, E., Calvo, P.M., Lafuente, J.V. (2019). Machine Learning Methods for Environmental-Enrichment-Related Variations in Behavioral Responses of Laboratory Rats. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-19591-5_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19590-8
Online ISBN: 978-3-030-19591-5
eBook Packages: Computer ScienceComputer Science (R0)