Sensory decoding in a tactile, interactive neurorobot

We present a novel neuromorphic robot that interacts through touch sensing and visual signaling on its surface. The robot´s form factor is a convex, hemispheric shell containing trackballs for sensing touch, and LEDs for communication with users. In this paper, we explore tactile sensory decoding by constructing a spiking neural network (SNN) of somatosensory cortex. The SNN uses a biologically inspired, unsupervised learning rule, known as spike timing dependent plasticity, to classify a user´s hand movements. In an evaluation of the network´s ability to categorize hand movements, both rate and temporal neural coding performed well. Because of its unique form factor and means of interaction, this robot, which is called CARL-SJR, may be useful for exploring the neural coding of touch, and also for Human-Robot Interaction studies.