In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …

Neuromorphic computing has been attracting ever‐increasing attention due to superior
energy efficiency, with great promise to promote the next wave of artificial general …

Constructing crossmodal in-sensor processing system based on high-performance flexible
devices is of great significance for the development of wearable human-machine interfaces …

It is widely believed the brain-inspired spiking neural networks have the capability of
processing temporal information owing to their dynamic attributes. However, how to …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Place recognition is an essential spatial intelligence capability for robots to understand and
navigate the world. However, recognizing places in natural environments remains a …

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic
chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the …

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature
dimensions in cellular morphology, structure, and composition. However, existing IACS …

Processing spatiotemporal data sources with both high spatial dimension and rich temporal
information is a ubiquitous need in machine intelligence. Recurrent neural networks in the …