The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Deep Learning (DL) systems have demonstrated unparalleled performance in many
challenging engineering applications. As the complexity of these systems inevitably …

Memristive devices have shown great promise to facilitate the acceleration and improve the
power efficiency of Deep Learning (DL) systems. Crossbar architectures constructed using …

We present and experimentally validate two minimal compact memristive models for spiking
neuronal signal generation using commercially available low-cost components. The first …

Recent years have seen deployments of increasingly complex artificial intelligent (AI) and
machine learning techniques being implemented on cloud server architectures and …

We present MEMprop, the adoption of gradient-based learning to train fully memristive
spiking neural networks (MSNNs). Our approach harnesses intrinsic device dynamics to …

This paper investigates the pinning synchronization of stochastic neutral memristive neural
networks with reaction–diffusion terms. Firstly, two novel pinning controllers, which contain …

As the energy and hardware investments necessary for conventional high‐precision digital
computing continue to explode in the era of artificial intelligence (AI), a change in paradigm …

This paper presents a series of multi-stage hybrid memristor-CMOS ternary combinational
logic stages that are optimized for reducing silicon area occupation. Prior demonstrations of …

In the realm of stability analysis for fractional neutral neural networks, it is not uncommon to
encounter erroneous Lyapunov functions. To investigate the stability of fractional memristor …