The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …

Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient
alternative to traditional artificial neural networks (ANNs). However, the performance gap …

Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …

Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …

With the gradual popularization of self-driving, it is becoming increasingly important for
vehicles to smartly make the right driving decisions and autonomously obey traffic rules by …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

We propose a new supervised learning rule for multilayer spiking neural networks (SNNs)
that use a form of temporal coding known as rank-order-coding. With this coding scheme, all …