Simulation of behavioral profiles in the plus-maze: A Classification and Regression Tree approach
This article introduces a simulation model of rat behavior in the elevated plus-maze, designed through a Decision trees approach using Classification and Regression algorithms. Starting from the analysis of the behavior performed by a sample of 18 Sprague-Dawley male rats, probabilistic rules descri...
|Main Authors:||Molina Delgado, Mauricio, Padilla Mora, Michael, Fornaguera Trías, Jaime|
This article introduces a simulation model of rat behavior in the elevated plus-maze, designed through a Decision trees approach using Classification and Regression algorithms. Starting from the analysis of the behavior performed by a sample of 18 Sprague-Dawley male rats, probabilistic rules describing behavioral patterns of the animals were extracted, and were used as the basis of the model computations. The model adequacy was tested by contrasting a simulated sample against an independent sample of real animals. Statistical tests showed that the simulated sample exhibits similar behaviors to those displayed by the real animals, both in terms of the number of entries to open and close arms as well as in terms of the time spent by the animals in those arms. However, the performance of the model in parameters related to the behavioral patterns was partially satisfactory. Given that previous attempts in the literature have neither include this kind of patterns nor the time as a crucial model parameter, the present model offers a suitable alternative for the computational simulation of this paradigm. Compared with antecedent models, the present simulation produced similar or better results in all the considered parameters. Beyond the goal of establish an appropriate simulational model, extracted rules also reveal important regularities associated to the rat behavior previously ignored by other models, i.e. that specific rat behaviors in the elevated plus-maze are time dependent. These and other important considerations to improve the model performance are discussed.