With their working mechanisms based on ion migration, the switching dynamics and
electrical behaviour of memristive devices resemble those of synapses and neurons, making …

The two possible pathways toward artificial intelligence (AI)—(i) neuroscience-oriented
neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Neuromorphic machines are intriguing for building energy-efficient intelligent systems,
where spiking neurons are pivotal components. Recently, memristive neurons with …

Developing a high-efficiency neuromorphic hardware network is essential to achieve
complex artificial intelligence. Here, for the first time, we propose a multi-neuromorphic …

Dense analog synaptic crossbar arrays are a promising candidate for neuromorphic
hardware accelerators due to the ability to mitigate data movement by performing in-situ …

Neuromorphic computing approaches become increasingly important as we address future
needs for efficiently processing massive amounts of data. The unique attributes of quantum …

As we approach the end of the silicon road map, alternative computing models that can
solve at-scale problems in the data-centric world are becoming important. This is …

Resistance random-access memory (RRAM) is a promising candidate for both the next-
generation non-volatile memory and the key element of neural networks. In this article …

We report the first experimental demonstration of ferroelectric field-effect transistor (FEFET)
based spiking neurons. A unique feature of the ferroelectric (FE) neuron demonstrated …