  Tactile sensing is essential for a variety of daily tasks. And recent
advances in event-driven tactile sensors and Spiking Neural Networks (SNNs)
spur the research in related fields. However, SNN-enabled event-driven tactile
learning is still in its infancy due to the limited representation abilities of
existing spiking neurons and high spatio-temporal complexity in the
event-driven tactile data. In this paper, to improve the representation
capability 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. Specifically, based on the classical Time Spike Response
Model (TSRM), we develop the Location Spike Response Model (LSRM). In addition,
based on the most commonly-used Time Leaky Integrate-and-Fire (TLIF) model, we
develop the Location Leaky Integrate-and-Fire (LLIF) model. Moreover, to
demonstrate the representation effectiveness of our proposed neurons and
capture the complex spatio-temporal dependencies in the event-driven tactile
data, we exploit the location spiking neurons to propose two hybrid models for
event-driven tactile learning. Specifically, the first hybrid model combines a
fully-connected SNN with TSRM neurons and a fully-connected SNN with LSRM
neurons. And the second hybrid model fuses the spatial spiking graph neural
network with TLIF neurons and the temporal spiking graph neural network with
LLIF neurons. Extensive experiments demonstrate the significant improvements of
our models over the state-of-the-art methods on event-driven tactile learning.
Moreover, compared to the counterpart artificial neural networks (ANNs), our
SNN models are 10x to 100x energy-efficient, which shows the superior energy
efficiency of our models and may bring new opportunities to the spike-based
learning community and neuromorphic engineering.
