29 citations · 91 across the 15 of their papers we have counts for
9 papers · 1 filter
A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma +3
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging bec…
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…
Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
Mingqing Xiao, Yixin Zhu, Di He +1
Spiking neural networks (SNNs) are investigated as biologically inspired models of neural computation, distinguished by their computational capability and energy efficiency due to…
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…
Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Shen Yan +3
Spiking Neural Networks (SNNs) are promising energy-efficient models for neuromorphic computing. For training the non-differentiable SNN models, the backpropagation through time (B…
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 High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation
Qingyan Meng, Mingqing Xiao, Shen Yan +3
Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs due to their n…
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…