A Neural Network Model of Multisensory Representation of Peripersonal Space: Effect of tool use

Abstract

This work describes an original neural network to simulate representation of the peripersonal space around one hand, in basal conditions and after training with a tool used to reach the far space. The model is composed of two unimodal areas (visual and tactile) connected to a third bimodal area (visual-tactile). Neurons in the bimodal area integrate visual and tactile information and are activated only when a stimulus falls inside the peripersonal space. Moreover, the model assumes that synapses linking unimodal to bimodal neurons can be reinforced by an Hebbian rule during training, but this reinforcement is also under the influence of attention mechanisms. Results show that the peripersonal space, which includes just a small visual space around the hand in normal conditions, becomes elongated in the direction of the tool after training. This expansion of the peripersonal space depends on an expansion of the visual receptive field of bimodal neurons, due to a reinforcement of visual synapses, which were just latent before training. The model may be of value to analyze the neural mechanisms responsible for representing and plastically shaping peripersonal space, and in perspective, for interpretation of psychophysical data on patients with brain damage.