Bio-mimetic strategies for tactile sensing

Abstract

In this work, a tactile sensing system is built for pattern recognition using spiking neurons. Tactile information is acquired using a fabric based binary tactile sensor array and converted into spatiotemporal spiking patterns that mimic mechanoreceptors in the skin. Through physical experiments, we show that the spike patterns efficiently represent information such as local curvature of objects in contact, which are easily distinguished using a supervised spike-timing based learning algorithm. High classification accuracy (>99%) and fast convergence rate (tens of epochs) of the classifier indicates good separation between different stimuli using the spatiotemporal spike representation.