Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Spiking neural networks (SNNs) have shown clear advantages over traditional artificial
neural networks (ANNs) for low latency and high computational efficiency, due to their event …

Abstract Spiking Neural Networks (SNNs) are considered a promising alternative to Artificial
Neural Networks (ANNs) for their event-driven computing paradigm when deployed on …

Spike-based machine intelligence has recently attracted increasing research attention, and
has been considered as a promising approach towards artificial general intelligence (AGI). It …

We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to
multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and …

The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led
to a growing interest in incorporating the self-attention mechanism and transformer-based …

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …

As the third-generation neural networks, spiking neural networks (SNNs) have great
potential on neuromorphic hardware because of their high energy efficiency. However, deep …

Until recently, the supply chain sector, which had been getting by with scattered
spreadsheets, phone conversations, and even paper-based records until recently, was …

Spiking neural networks (SNNs) operating with asynchronous discrete events show higher
energy efficiency with sparse computation. A popular approach for implementing deep …