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 Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …

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

Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal
dynamics, which can only be simulated serially and can hardly learn long-time …

The field of neuromorphic computing promises extremely low-power and low-latency
sensing and processing. Challenges in transferring learning algorithms from traditional …

Spiking neural networks (SNNs) are gaining increasing attention as potential
computationally efficient alternatives to traditional artificial neural networks (ANNs) …

Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …

Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of
neuromorphic computing owing to their low energy consumption during feedforward …

Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics
demonstrate superior energy efficiency on neuromorphic architectures. Error …