通过增强时间一致性提高尖峰神经网络的稳定性和性能

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pattern Recognition Pub Date : 2024-10-26 DOI:10.1016/j.patcog.2024.111094
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引用次数: 0

摘要

尖峰神经网络因其类似大脑的信息处理能力而备受关注。代梯度的使用使得利用反向传播训练尖峰神经网络成为可能,从而在各种任务中取得了令人印象深刻的性能。然而,使用反向传播训练的尖峰神经网络通常使用平均输出来逼近实际标签,因此往往需要更大的模拟时间步来提高网络性能。这种延迟限制为尖峰神经网络的进一步发展带来了挑战。当前的训练算法往往会忽略不同时间步的输出分布差异。特别是对于神经形态数据集而言,不同时间步的输入会导致输出分布的不一致性,从而导致在结合不同时刻的优化方向时与最优方向产生显著偏差。为了解决这个问题,我们设计了一种方法来增强不同时间步输出的时间一致性。我们在 CIFAR10、CIFAR100 和 ImageNet 等静态数据集上进行了实验。结果表明,我们的算法可以达到与其他最优 SNN 算法相当的性能。值得注意的是,我们的算法在神经形态数据集 DVS-CIFAR10 和 N-Caltech101 上取得了最先进的性能,并能在时间步 T = 1 的测试阶段取得优异的性能。
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Improving stability and performance of spiking neural networks through enhancing temporal consistency
Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train spiking neural networks with backpropagation, leading to impressive performance in various tasks. However, spiking neural networks trained with backpropagation typically approximate actual labels using the average output, often necessitating a larger simulation timestep to enhance the network’s performance. This delay constraint poses a challenge to the further advancement of spiking neural networks. Current training algorithms tend to overlook the differences in output distribution at various timesteps. Particularly for neuromorphic datasets, inputs at different timesteps can cause inconsistencies in output distribution, leading to a significant deviation from the optimal direction when combining optimization directions from different moments. To tackle this issue, we have designed a method to enhance the temporal consistency of outputs at different timesteps. We have conducted experiments on static datasets such as CIFAR10, CIFAR100, and ImageNet. The results demonstrate that our algorithm can achieve comparable performance to other optimal SNN algorithms. Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T = 1.
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来源期刊
Pattern Recognition
Pattern Recognition 工程技术-工程:电子与电气
CiteScore
14.40
自引率
16.20%
发文量
683
审稿时长
5.6 months
期刊介绍: The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.
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