The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient
processing and computing exploiting spiking-driven and sparsity features of biological …

With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …

The spiking neural network (SNN) mimics the information-processing operation in the
human brain. Directly applying backpropagation to the training of the SNN still has a …

Spiking neural networks (SNNs) is a promising learning model due to its computational
efficiency for discrete spike events. However, because of the binary output of spiking …

Spiking neural networks (SNNs) have increasingly drawn massive research attention due to
biological interpretability and efficient computation. Recent achievements are devoted to …

Event-based neuromorphic systems promise to reduce the energy consumption of deep
learning tasks by replacing expensive floating point operations on dense matrices by low …

The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …

Spiking neural networks (SNNs) have manifested remarkable advantages in power
consumption and event-driven property during the inference process. To take full advantage …

While Backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving
encouraging results, a key challenge involved is to backpropagate a differentiable …

Spiking neural networks (SNNs) with event-based computation are promising brain-inspired
models for energy-efficient applications on neuromorphic hardware. However, most …