Modelling Rat Behavior in an Elevated Plus-Maze Confronted with Experimental Data
This work describes a neural network model of rat exploratory behavior in the elevated plus-maze, a test used to study anxiety. It involves three parameters: drive to explore, drive to avoid aversive stimuli and spontaneous locomotive activity. Competitive learning is used to generate a sequence of network states each corresponding to a place in the maze. The work also presents experiments made with real rats providing data to be compared with the simulation results. The simulations are consistent with the experimental evidence, and may provide an efficient way of describing anxiety-like behaviors of rats in the elevated plus-maze.
KeywordsExploratory Behavior Competitive Learning Exploratory Motivation Virtual Animal Virtual Maze
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