Tactile Surface Roughness Categorization With Multineuron Spike Train Distance

Abstract

Tactile sensing with spiking neural networks (SNNs) has attracted increasing attention in the past decades. In this article, a novel SNN framework is proposed for the tactile surface roughness categorization task. In contrast to supervised SNN methods such as ReSuMe and Tempotron that require prespecifying target spike trains, the presented method performs the classification through directly comparing the distance between multineuron spike trains. Unlike simple spike train fusion methods using average pairwise spike train distance or pooled spike train distance, the proposed method merges spike trains from different neurons with the multineuron spike train distance, which can capture the complex correlation of multiple spike trains. Specifically, the spike trains are generated via the Izhikevich neurons from tactile signals. The similarity of the multineuron spike trains is computed using the multineuron Victor–Purpura spike train distance, which can be efficiently implemented in an inductive manner. The classification can be performed by incorporating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -nearest neighbors and the multineuron spike train distance as a similarity metric. The proposed framework is quite general, i.e., other multineuron spike train distances and spike train kernel-based methods can be readily incorporated. The effectiveness of the proposed method has been demonstrated on a tactile data set by comparing it with various feature- and spike-based methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In the soft neuromorphic implementation of biomimetic tactile sensing and the development of the tactile sensing capability in neurobotic systems, the processing and analysis of spike-like tactile signals are quite common. Inspired by human tactile perception, this article proposes a novel supervised spiking neural network method for tactile sensing tasks. The traditional methods have to prespecify target spike trains, which is still an open question. In addition, the current ways to fuse spike trains from multiple neurons are far from mature. This article tackles these two problems using spike train similarity comparison with multineuron spike train distance. The direct spike train similarity comparison avoids the need to prespecify target spike trains. The multineuron spike train distance can inherently fuse spike trains from different neurons. It is demonstrated that the proposed method is able to effectively perform classification in a tactile roughness discrimination task.