Today state-of-the-art deep neural networks that process event-camera data first convert a
temporal window of events into dense grid-like input representations. As such they exhibit …

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

The real-world use cases of Machine Learning (ML) have exploded over the past few years.
However, the current computing infrastructure is insufficient to support all real-world …

We introduce a supervised learning algorithm for photonic spiking neural network (SNN)
based on back propagation. For the supervised learning algorithm, the information is …

Abstract Spiking Neural Networks (SNNs), serving as a nexus between neuroscience and
machine learning, strive to emulate the intricacies of biological neurons. Their remarkable …

This paper investigates the vulnerability of spiking neural networks (SNNs) and federated
learning (FL) to backdoor attacks using neuromorphic data. Despite the efficiency of SNNs …

In the field of computer vision, event-based Dynamic Vision Sensors (DVSs) have emerged
as a significant complement to traditional pixel-based imaging due to their low power …

With the future 6G era, spiking neural networks (SNNs) can be powerful processing tools in
various areas due to their strong artificial intelligence (AI) processing capabilities, such as …

Abstract Spiking Neural Networks (SNNs) are increasingly deployed in applications on
resource constraint embedding systems due to their low power. Unfortunately, SNNs are …

While security vulnerabilities in traditional Deep Neural Networks (DNNs) have been
extensively studied, the susceptibility of Spiking Neural Networks (SNNs) to adversarial …