A Spiking Neural Network With Spike-Timing-Dependent Plasticity for Surface Roughness Analysis

Abstract

Spiking neural network (SNN) utilizes spike trains for information processing among neurons, which is more biologically plausible and widely regarded as the third-generation artificial neural network (ANN). It has the potential for effectively processing spatial-temporal information and has the characteristics of lower power consumption and smaller calculation load compared with conventional ANNs. In this work, we demonstrate the feasibility of applying SNN to classify tactile signals collected by a bionic artificial fingertip that touches a group of real-world metal surfaces with different roughness levels. A two-layer SNN is adopted and trained using an unsupervised learning method with spike-timing-dependent plasticity (STDP). Experiments show that the trained SNN can categorize the input tactile signals into different surface roughness of metal textures with more than 80% accuracy. This work lays the foundation of applying SNNs to more complex tactile signal processing in robotics, manufacturing, and other engineering fields.