Event-Driven Tactile Learning with Location Spiking Neurons

TL;DR

This paper introduces location spiking neurons and a hybrid SNN model to enhance event-driven tactile learning, achieving better accuracy and energy efficiency on neuromorphic hardware.

Contribution

It proposes a novel location spiking neuron model and a hybrid SNN architecture to improve feature extraction and spatio-temporal dependency modeling in event-driven tactile data.

Findings

Significant accuracy improvements over existing methods

Enhanced energy efficiency on neuromorphic hardware

Effective modeling of complex spatio-temporal dependencies

Abstract

The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model - Location Spike Response Model (LSRM) that serves as a new building block of SNNs. Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware.