In biological neural systems, different neurons are capable of self-organizing to form
different neural circuits for achieving a variety of cognitive functions. However, the current …

Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …

The backpropagation algorithm has promoted the rapid development of deep learning, but it
relies on a large amount of labeled data and still has a large gap with how humans learn …

Spiking Neural Networks are often touted as brain-inspired learning models for the third
wave of Artificial Intelligence. Although recent SNNs trained with supervised …

High-quality and challenging event stream datasets play an important role in the design of
an efficient event-driven mechanism that mimics the brain. Although event cameras can …

Traditional deep learning relies on end-to-end backpropagation for training, but it suffers
from drawbacks such as high memory consumption and not aligning with biological neural …

As the scales of neural networks increase, techniques that enable them to run with low
computational cost and energy efficiency are required. From such demands, various efficient …

Spiking Neural Networks (SNNs) have attracted significant attention from researchers across
various domains due to their brain-inspired information processing mechanism. However …

Spiking neural networks (SNNs) have been widely used due to their strong biological
interpretability and high-energy efficiency. With the introduction of the backpropagation …

Studies suggest that the brain's high efficiency and low energy consumption may be closely
related to its small-world topology and critical dynamics. However, existing efforts on the …