The peripersonal space may be considered as the action space within which the body directly interacts with external objects. There is converging evidence that peripersonal space is represented by a specialized circuit of multimodal neurons integrating tactile stimuli applied on a body part with visual stimuli delivered near the same body part, e.g. the hand. Tools used to extend the action space may modify the boundaries of the peri-hand area, where vision and touch are integrated. In extinction patients, a far visual stimulus at the tip of a right-held tool produces left tactile extinction similar to a near visual stimulus at the hand, only after the patients have used the tool to reach the far space. The neural mechanisms underlying such plasticity have not been yet identified. To this aim, neural network modeling may be integrated with the experimental research. In this work, we pursued two main objectives: i) to use an artificial neural network in order to postulate some physiological mechanisms for peri-hand plasticity able to account for in-vivo data; ii) to validate the artificial network by testing predictions derived from simulations with an ad hoc behavioural experiment on an extinction patient.
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