Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Ion migration as well as electron transfer and coupling in resistive switching materials
endow memristors with a physically tunable conductance to resemble synapses, neurons …

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

Synapses are essential for the transmission of neural signals. Synaptic plasticity allows for
changes in synaptic strength, enabling the brain to learn from experience. With the rapid …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Learning is traditionally studied in biological or computational systems. The power of
learning frameworks in solving hard inverse problems provides an appealing case for the …

Facing the exponential growth of data digital communications and the advent of artificial
intelligence, there is an urgent need for information technologies with huge storage capacity …

Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical
way, by enabling machine learning in the industry, business, health, transportation, and …

At the end of a rush lasting over half a century, in which CMOS technology has been
experiencing a constant and breathtaking increase of device speed and density, Moore's …