Research on electronic devices and materials is currently driven by both the slowing down
of transistor scaling and the exponential growth of computing needs, which make present …

Artificial neurons and synapses are considered essential for the progress of the future brain‐
inspired computing, based on beyond von Neumann architectures. Here, a discussion on …

Reservoir computing offers a powerful neuromorphic computing architecture for
spatiotemporal signal processing. To boost the power efficiency of the hardware …

The effective mimicry of neurons is key to the development of neuromorphic electronics.
However, artificial neurons are not typically capable of operating in biological environments …

Neuromorphic computing memristors are attractive to construct low-power-consumption
electronic textiles due to the intrinsic interwoven architecture and promising applications in …

Neuromorphic computing, a computing paradigm inspired by the human brain, enables
energy-efficient and fast artificial neural networks. To process information, neuromorphic …

Many in-memory computing frameworks demand electronic devices with specific switching
characteristics to achieve the desired level of computational complexity. Existing memristive …

Integrating neurons into digital systems may enable performance infeasible with silicon
alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive …

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while
silicon image sensors do not. The energy-budget mismatch between biological systems and …

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