Spiking neural networks combine analog computation with event-based communication
using discrete spikes. While the impressive advances of deep learning are enabled by …

Recent research resolves the challenging problem of building biophysically plausible
spiking neural models that are also capable of complex information processing. This …

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 …

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is
motivating the search for high-performance and efficient spiking neural networks to run on …

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

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated
to perform efficiently in a variety of applications, such as dimensionality reduction, feature …

We introduce an algorithm to do backpropagation on a spiking network. Our network is"
spiking" in the sense that our neurons accumulate their activation into a potential over time …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

On-chip edge intelligence has necessitated the exploration of algorithmic techniques to
reduce the compute requirements of current machine learning frameworks. This work aims …