Neuromorphic computing is a brain-inspired computing paradigm that aims to construct
efficient, low-power, and adaptive computing systems by emulating the information …

In the biological nervous system, the integration and cooperation of parallel system of
receptors, neurons, and synapses allow efficient detection and processing of intricate and …

Emulating the auditory sense is a significant challenge in terms of both integration and
energy consumption for handling complicated spatiotemporal information. Here, we …

Brain‐inspired neuromorphic computing has been developed as a potential candidate for
solving the von Neumann bottleneck of traditional computing systems. 2D materials‐based …

Event cameras are emerging vision sensors and their advantages are suitable for various
applications such as autonomous robots. Contrast maximization (CMax), which provides …

The ever-increasing volume of complex data poses significant challenges to conventional
sequential global processing methods, highlighting their inherent limitations. This …

With scalability and diverse device behavior, memristors present potential for executing
neuromorphic computation with lower hardware cost and power consumption. However …

Inspired by the functions of biological neural networks, volatile memristors are essential for
implementing neuromorphic computing. These devices enable large-scale and energy …

Cochleas are the basis for biology to process and recognize speech information, emulating
which with electronic devices helps us construct high-efficient intelligent voice systems …

Spike-based neural networks, which use spikes or action potentials to represent information,
have gained a lot of attention because of their high energy efficiency and low power …