The industrial Internet of Things (IIoT) significantly enhances mechanical fault diagnosis.
However, IIoT-based intelligent diagnostic models struggle with sample insufficiency and …

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most
cortical activity is internally generated by recurrence. Both theoretical and experimental …

Spiking neural networks can compensate for quantization error by encoding information
either in the temporal domain, or by processing discretized quantities in hidden states of …

Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial
neural networks (ANNs) once again. They have become the state-of-the-art models and …

The microring resonator is a ubiquitous building block of optical integrated circuits. Owing to
its unique non-linear properties, it appears well-suited as a node in the realization of …

The circuitry and pathways in the brains of humans and other species have long inspired
researchers and system designers to develop accurate and efficient systems capable of …

Spiking neural networks (SNNs) are brain-inspired event-driven models of computation with
promising ultra-low energy dissipation. Rich network dynamics emergent in recurrent …

The central nervous system receives a vast amount of sensory inputs, and it should be able
to discriminate and recognize different kinds of multisensory information. Winner‐take‐all …

Abstract Spiking Neural Networks (SNNs) can closely mimic the biological neural network
systems. Recently, the SNNs have been developed in hardware circuits to emulate the time …

Spiking neural networks (SNNs) offer a promising biologically-plausible computing model
and lend themselves to ultra-low-power event-driven processing on neuromorphic …