Abstract
In this paper we propose the use of an artificial neural network associated to a genetic algorithm to develop a behavioral model of rats in elevated plus-maze. The main novelty is the fitness function used, which is independent of prior known experimental data. Our results agree with experimental tests, demonstrating that open arms exploration evoke greater avoidance. The perspective of the results are increased by analyzing Markov chains obtained by experiments with real rats and by computational simulations, suggesting that the general fitness function proposed summarizes the main relevant characteristics for the study of the rats behavior in the elevated plus-maze.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hogg, S.: A review of the validity and variability of the elevated plus-maze as an animal model of anxiety. Pharmacol. Biochem. Behav. 54(1), 21–30 (1996)
Montgomery, K.C.: The relation between fear induced by novel stimulation and exploratory behavior. J. Comp. Physiol. Psychol. 48, 254–260 (1955)
Graeff, F.G.: Brain defense systems and anxiety. In: Burrows, G.D., Roth, M., Noyes, R. (eds.) Handbook of Anxiety. The Neurobiology of Anxiety, vol. 3, pp. 307–354. Elsevier, Amsterdam (1990)
Pellow, S., Chopin, P., File, S.E., Briley, M.: Validation of open closed arm entries in an elevated plus-maze as a measure of anxiety in the rat. J. Neurosci. Methods 14(3), 147–167 (1985)
Salum, C., Roque-da-Silva, A.C., Morato, S.: Conflict as a determinant of rat behavior in three types of elevated plus-maze. Behav. Processes 63, 87–93 (2003)
Walf, A.A., Frye, C.A.: The use of the elevated plus maze as an assay of anxiety-related behavior in rodents. Nature Protocols 2, 322–328 (2007)
Salum, C., Roque-da-Silva, A.C., Morato, S.: Anxiety-like behavior in rats: a computational model. Neural Netw. 13(1), 21–29 (2000)
Giddings, J. M.: Modeling the Behavior of Rats in an Elevated Plus-Maze. Master’s thesis. Acadia University (acess in April 11, 2012)
Shimo, H.K., Tejada, J., Roque, A.C., Morato, S., Tinós, R.: Use of evolutionary robots as an auxiliary tool for developing behavioral models of rats in an elevated plus-maze. In: 10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks (2010)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press/Bradford Books, Cambridge (2000)
Tejada, J., Bosco, G.G., Morato, S., Roque, A.C.: Characterization of the rat exploratory behavior in the elevated plus-maze with Markov chains. J. Neurosci. Methods 193(2), 288–295 (2010)
Kemeny, J., Snell, J.: Finite Markov chains. Springer, NJ (1976)
Haccou, P., Dienske, H., Meelis, E.: Analysis of time-inhomogeneity in Markov chains applied to mother-infant interactions of rhesus monkeys. Animal Behaviour 31(3), 927–945 (1983)
Garcia-Perez, E., Mazzoni, A., Zoccolan, D., Robinson, H.P.C., Torre, V.: Statistics of decision making in the leech. J. Neurosci. 25, 2597–2608 (2005)
Yang, H., Chao, A.: Modeling Animals’ Behavioral Response by Markov Chain Models for Capture–Recapture Experiments. Biometrics 61(4), 1010–1017 (2005)
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley Publishing Company (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Costa, A.A., Roque, A.C., Morato, S., Tinós, R. (2012). A Model Based on Genetic Algorithm for Investigation of the Behavior of Rats in the Elevated Plus-Maze. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)