Hybrid neuromorphic computing supporting the prevailing artificial neural networks and
neuroscience-inspired models/algorithms offers substantial flexibility for cross-paradigm …

This research article reports a novel off–the-shelf floating memristor circuit based on MOS
transistor. The proposed memristor circuit comprises one NMOS, one PMOS and a constant …

Data augmentation is an effective way to overcome the over-fitting problem of deep learning
models. However, most existing studies on data augmentation work on frame-like data (eg …

Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of
artificial neural network (ANN) based AI applications. As the progress in neuromorphic …

The goal of textual adversarial attack methods is to replace some words in an input text in
order to make the victim model misbehave. This article proposes an effective word-level …

Abstract Spiking Neural Network (SNN) has been recognized as the third generation of
neural networks. Conventionally, a SNN can be converted from a pre-trained Artificial Neural …

The demand for neuromorphic chips has skyrocketed in recent years. Thus, efficient
manufacturing testing becomes an issue. Conventional testing cannot be applied because …

Event-based dynamic vision sensors provide very sparse output in the form of spikes, which
makes them suitable for low-power applications. Convolutional spiking neural networks …

In this paper, we propose a novel way for power amplifiers (PA) modeling using spiking
neurons. The rate of neurons firing spikes is a nonlinear function of its excitation current …

Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …