Spike Count Maximization for Neuromorphic Vision Recognition

Jianxiong Tang, Jianhuang Lai, Xiaohua Xie, Lingxiao Yang
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Abstract

Spiking Neural Networks (SNNs) are the promising models of neuromorphic vision recognition. The mean square error (MSE) and cross-entropy (CE) losses are widely applied to supervise the training of SNNs on neuromorphic datasets. However, the relevance between the output spike counts and predictions is not well modeled by the existing loss functions. This paper proposes a Spike Count Maximization (SCM) training approach for the SNN-based neuromorphic vision recognition model based on optimizing the output spike counts. The SCM is achieved by structural risk minimization (SRM) and a specially designed spike counting loss. The spike counting loss counts the output spikes of the SNN by using the L0-norm, and the SRM maximizes the distance between the margin boundaries of the classifier to ensure the generalization of the model. The SCM is non-smooth and non-differentiable, and we design a two-stage algorithm with fast convergence to solve the problem. Experiment results demonstrate that the SCM performs satisfactorily in most cases. Using the output spikes for prediction, the accuracies of SCM are 2.12%~16.50% higher than the popular training losses on the CIFAR10-DVS dataset. The code is available at https://github.com/TJXTT/SCM-SNN.
神经形态视觉识别的峰值计数最大化
脉冲神经网络(SNNs)是神经形态视觉识别中很有前途的模型。均方误差(MSE)和交叉熵(CE)损失被广泛应用于监督snn在神经形态数据集上的训练。然而,输出尖峰计数和预测之间的相关性并没有很好地由现有的损失函数建模。针对基于snn的神经形态视觉识别模型,提出了一种基于输出尖峰数优化的尖峰数最大化训练方法。单片机是通过结构风险最小化(SRM)和特殊设计的尖峰计数损耗来实现的。尖峰计数损失利用l0范数对SNN的输出尖峰进行计数,SRM通过最大化分类器的边际边界之间的距离来保证模型的泛化。单片机是非光滑不可微的,设计了一种快速收敛的两阶段算法来解决该问题。实验结果表明,该单片机在大多数情况下都具有令人满意的性能。使用输出尖峰进行预测,SCM的准确率比CIFAR10-DVS数据集上流行的训练损失高2.12%~16.50%。代码可在https://github.com/TJXTT/SCM-SNN上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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