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.
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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
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DOI: https://doi.org/10.1007/978-3-642-32639-4_19
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