Event-Driven Tactile Learning with Location Spiking Neurons

• URL: http://arxiv.org/abs/2209.01080v1
• Date: Sat, 23 Jul 2022 12:15:43 GMT
• Title: Event-Driven Tactile Learning with Location Spiking Neurons
• Authors: Peng Kang, Srutarshi Banerjee, Henry Chopp, Aggelos Katsaggelos, Oliver Cossairt
• Abstract summary: Spiking Neural Networks (SNNs) enable event-driven tactile learning. We develop a novel neuron model called "location spiking neuron" We show the superior energy efficiency of our models over other works on event-driven learning.
• Score: 5.822511654546528
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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