Spiking neural networks (SNNs), a brain-inspired computing paradigm, are emerging for
their inference performance, particularly in terms of energy efficiency and latency attributed …

Spiking neural networks (SNN) are a biologically inspired model of neural networks with
certain brain-like properties. In the past few decades, this model has received increasing …

Robotic systems start to coexist around humans but cannot physically interact as humans do
due to the absence of tactile sensitivity across their bodies. Various studies have developed …

Human employs different touch patterns to convey diverse social messages; for example, a
stroke is an encouragement, whereas a hit is an offense. Various tactile sensors have been …

Tactile object shapes are considered as important properties in robotic manipulation. Many
researches have focused recently on using tactile sensing systems to enable tactile …

This paper presents a neuromorphic computing model that classifies material textures using
a neural coding scheme based on threshold encoding. The proposed threshold encoding …

This paper presents a TinyML system based on a multisensory glove for touch modality
classification. It employs a glove equipped with five tactile sensors and five IMUs to collect a …

Abstract Spiking Neural Networks (SNNs) have become the most promising method to solve
machine learning-based problems due to their biological and hardware plausibility and …

Abstract Recently, Spiking Neural Networks (SNNs) have been considered as alternatives to
the common deep neural networks (DNNs) when the energy efficiency has been targeted …

Recent advancements in Artificial Intelligence and machine learning methods have been the
focus of much research in different application domains due to their possibility to enable …