The elevated plus-maze is an apparatus widely used to study the level of anxiety in rodents. The maze is plus-shaped, with two enclosed arms and two open arms, and elevated 50cm from the floor. During a test, which usually lasts for 5min, the animal is initially put at the center and is free to move and explore the entire maze. The level of anxiety is measured by variables such as the percentage of time spent and the number of entries in the enclosed arms. High percentage of time spent at and number of entries in the enclosed arms indicate anxiety. Here we propose a computational model of rat behavior in the elevated plus-maze based on an artificial neural network trained by a genetic algorithm. The fitness function of the genetic algorithm is composed of reward (positive) and punishment (negative) terms, which are incremented as the computational agent (virtual rat) moves in the maze. The punishment term is modulated by a parameter that simulates the effects of different drugs. Unlike other computational models, the virtual rat is built independently of prior known experimental data. The exploratory behaviors generated by the model for different simulated pharmacological conditions are in good agreement with data from real rats.
Keywords: Artificial neural network; Computational model; Elevated plus-maze; Exploratory behavior; Genetic algorithm; Rat.
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