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

Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …

In the recent quest for trustworthy neural networks, we present Spiking Neural Network
(SNN) as a potential candidate for inherent robustness against adversarial attacks. In this …

Spiking neural network (SNN) is broadly deployed in neuromorphic devices to emulate brain
function. In this context, SNN security becomes important while lacking in-depth …

Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …

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

Deep neural networks (DNNs) have demonstrated remarkable performance across various
tasks, including image and speech recognition. However, maximizing the effectiveness of …

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 …

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

In this era of machine learning models, their functionality is being threatened by adversarial
attacks. In the face of this struggle for making artificial neural networks robust, finding a …