Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for
recognizing multivariate responses obtained by gas sensors in various applications. Over …

Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …

In biological neural systems, different neurons are capable of self-organizing to form
different neural circuits for achieving a variety of cognitive functions. However, the current …

Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

Abstract Most existing Spiking Neural Network (SNN) works state that SNNs may utilize
temporal information dynamics of spikes. However, an explicit analysis of temporal …

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …

As spiking neural networks (SNNs) are deployed increasingly in real-world efficiency critical
applications, the security concerns in SNNs attract more attention. Currently, researchers …

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …