The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

Spiking neural networks (SNNs) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …

Neuromorphic data, recording frameless spike events, have attracted considerable attention
for the spatiotemporal information components and the event-driven processing fashion …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

A self-organizing map (SOM) is a powerful unsupervised learning neural network for
analyzing high-dimensional data in various applications. However, hardware …

Artificial neural networks (ANN) have become the mainstream acoustic modeling technique
for large vocabulary automatic speech recognition (ASR). A conventional ANN features a …

Brain-inspired computing, drawing inspiration from the fundamental structure and
information-processing mechanisms of the human brain, has gained significant momentum …

Artificial neural networks (ANNs) are the basis of recent advances in artificial intelligence
(AI); they typically use real valued neuron responses. By contrast, biological neurons are …

Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed
and ultra-low energy consumption because of their binary and event-driven nature …