用于高性能脉冲神经网络的带同步重置的噪声组神经元
Noisy group neurons with synchronous resetting for high-performance spiking neural networks
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中文总结 AI 辅助
针对深度脉冲神经网络训练的时空信息丢失与梯度不匹配问题，提出带同步重置和神经随机性的噪声组神经元模型，结合平均场反向传播方法，在多数据集上验证其高性能，尤其在CIFAR10-DVS上10步推理达87.35%准确率，为神经形态计算提供实用方案。
中文摘要 AI 辅助
脉冲神经网络（SNN）具有生物启发的神经元动力学和事件驱动通信特性，近年来已取得显著进展。然而，由于时空信息丢失和梯度不匹配，深度SNN的训练仍具挑战性。为同时解决这些问题，我们提出噪声组神经元（NGN）模型，将群体级同步重置和神经随机性作为基本计算机制。随后，我们开发NGN方法作为框架，结合NGN模型与基于平均场动力学的反向传播学习。我们通过理论分析及在CIFAR-10、CIFAR-100、Tiny-ImageNet、DVS-Gesture、N-Caltech101和CIFAR10-DVS上的实验验证，证明NGN方法的优势。该方法在10个推理时间步内，于CIFAR10-DVS上达到87.35%的准确率。这些结果表明，NGN是一种适用于高性能神经形态计算的实用方法。
英文摘要
Spiking neural networks (SNNs), characterized by bio-inspired neuronal dynamics and event-driven communication, have attained significant progress in recent years. Nevertheless, training deep SNNs remains challenging due to spatiotemporal information loss and gradient mismatching. To simultaneously address these issues, we propose a noisy group neuron (NGN) model, which incorporates population-level synchronous resetting and neural stochasticity as fundamental computational mechanisms. We then develop the NGN method as a framework that combines the NGN model with backpropagation learning based on mean-field dynamics. We demonstrate the advantages of the NGN method through theoretical analysis and experimental validation on CIFAR-10, CIFAR-100, Tiny-ImageNet, DVS-Gesture, N-Caltech101, and CIFAR10-DVS. The proposed approach achieves an accuracy of 87.35% on CIFAR10-DVS within 10 inference time steps. These results support NGN as a practical approach to high-performance neuromorphic computing.
发表机构
- School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学院）
- Center for Intersection of Mathematics and Life Sciences, Xi’an Jiaotong University（西安交通大学数学与生命科学交叉中心）
- School of Life Science and Technology, Xi’an Jiaotong University（西安交通大学生命科学与技术学院）
- Research Center for Brain-inspired Intelligence, Xi’an Jiaotong University（西安交通大学类脑智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。
