29 citations · 53 across the 6 of their papers we have counts for
5 papers · 1 filter
Online Pseudo-Zeroth-Order Training of Neuromorphic Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training SNNs in a more biol…
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually lear…
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) with event-based computation are promising brain-inspired models for energy-efficient applications on neuromorphic hardware. However, most supervised…
Online Training Through Time for Spiking Neural Networks
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with…
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Mingqing Xiao, Qingyan Meng, Zongpeng Zhang +2
Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a ha…