A Stochastic Spiking Network Model of Sensorimotor Control

We demonstrate the implementation of a new spike-based control scheme on an Omni robot arm. Our robotic demonstrations replicate a variety of complex human motor behavior, such as reaching and tracking, reflexes in task-relevant directions, and reward/penalty trade-off responses. Our scheme can provide a neurophysiological explanation of specific human sensorimotor functions under uncertainty.

Users can see and try out spike-based tracking ability of the arm using spheres representing reward (wanted) regions. They can also try and feel the robot's response to perturbations. They can try the robot's response to the simultaneous presence of reward (wanted) and penalty (feared) regions.