Satisfying the rapid evolution of artificial intelligence (AI) algorithms requires exponential
growth in computing resources, which, in turn, presents huge challenges for deploying AI …

Brain–computer interfaces (BCIs) that enable human–machine interaction have immense
potential in restoring or augmenting human capabilities. Traditional BCIs are realized based …

Cutting-edge mid-wavelength infrared (MWIR) sensing technologies leverage infrared
photodetectors, memory units, and computing units to enhance machine vision. Real-time …

The brain's connectivity is locally dense and globally sparse, forming a small-world graph—
a principle prevalent in the evolution of various species, suggesting a universal solution for …

Complex-valued neural networks process both amplitude and phase information, in contrast
to conventional artificial neural networks, achieving additive capabilities in recognizing …

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal
neurons for non-linear computations and signal processing. Dendritic branches facilitate …

Generative artificial intelligence (GAI) requires substantial computational resources for
model training and inference, but the electronic-waste (e-waste) implications of GAI and its …

Low‐power and parallel processing Neuromorphic computing that imitates on the human
brain's extraordinary data processing and learning capabilities is promising in non‐von …

Neuromorphic speech recognition systems that use spiking neural networks (SNNs) and
memristors are progressing in hardware development. The conventional manual …

Standard deep learning algorithms require differentiating large nonlinear networks, a
process that is slow and power-hungry. Electronic contrastive local learning networks …