Abstract
Several experiment done by the researchers conducted that autism is caused by the dysfunctional mirror neuron system and the dysfunctions of mirror neuron system is proportional to the symptom severity of autism. In the present work those experiments were studied as well as studying a model of mirror neuron system called MNS2 developed by a research group. This research examined the behavior of the model in case of autism and compared the result with those studies conducting dysfunctions of mirror neuron system in autism. To perform this, a neural network employing the model was developed which recognized the three types of grasping (faster, normal and slower). The network was implemented with back propagation through time learning algorithm. The whole grasping process was divided into 30 time steps and different hand and object states at each time step was used as the input of the network. Normally the network successfully recognized all of the three types of grasps. The network required more times as the number of inactive neurons increased. And in case of maximum inactive neurons of the mirror neuron system the network became unable to recognize the types of grasp. As the time to recognize the types of grasp is proportional to the number of inactive neurons, the experiment result supports the hypothesis that dysfunctions of MNS is proportional to the symptom severity of autism.
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