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 …

With the growth of real-time and latency-sensitive applications in the Internet of Everything
(IoE), service placement cannot rely on cloud computing alone. In response to this need …

The next generation of machine intelligence requires the capability of continual learning to
acquire new knowledge without forgetting the old one while conserving limited computing …

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be
great computation-efficient models. The spiking neurons encode beneficial temporal facts …

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-
temporal patterns in time series, such as the Address-Event Representation data collected …

The state prediction of key components in manufacturing systems tends to be risk-sensitive
tasks, where prediction accuracy and stability are the two key indicators. The physics …

We propose a new learning framework, signal propagation (sigprop), for propagating a
learning signal and updating neural network parameters via a forward pass, as an …

Spiking neural networks (SNNs), known for their low-power, event-driven computation, and
intrinsic temporal dynamics, are emerging as promising solutions for processing dynamic …

The artificial neural network-spiking neural network (ANN-SNN) conversion, as an efficient
algorithm for deep SNNs training, promotes the performance of shallow SNNs, and expands …

This paper introduces a novel approach to enhance the stability and efficiency of R-STDP in
the context of federated learning. The primary objective is to stabilize the unbounded growth …