A survey of encoding techniques for signal processing in spiking neural networks
Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …
intelligence due to their ability to solve complex problems while being power efficient. They …
Research progress of spiking neural network in image classification: a review
LY Niu, Y Wei, WB Liu, JY Long, T Xue - Applied intelligence, 2023 - Springer
Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …
which is more analogous with the brain. It has been widely considered with neural …
[PDF][PDF] LISNN: Improving spiking neural networks with lateral interactions for robust object recognition.
Abstract Spiking Neural Network (SNN) is considered more biologically plausible and
energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm …
energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm …
Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding
The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …
algorithm has made significant progress in recent years. Among the various training …
A survey on neuromorphic computing: Models and hardware
The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …
on traditional computer systems. As the performance of traditional Von Neumann machines …
Efficient and accurate conversion of spiking neural network with burst spikes
Near lossless transfer learning for spiking neural networks
Spiking neural networks (SNNs) significantly reduce energy consumption by replacing
weight multiplications with additions. This makes SNNs suitable for energy-constrained …
weight multiplications with additions. This makes SNNs suitable for energy-constrained …
Temporal-coded deep spiking neural network with easy training and robust performance
Spiking neural network (SNN) is promising but the development has fallen far behind
conventional deep neural networks (DNNs) because of difficult training. To resolve the …
conventional deep neural networks (DNNs) because of difficult training. To resolve the …
SSTDP: Supervised spike timing dependent plasticity for efficient spiking neural network training
Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power
event-driven neuromorphic hardware due to their spatio-temporal information processing …
event-driven neuromorphic hardware due to their spatio-temporal information processing …