改善尖峰神经网络学习性能的自适应突触调整机制

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hyun-Jong Lee, Jae-Han Lim
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引用次数: 0

摘要

尖峰神经网络(SNN)因其在各种任务中的高效性而备受研究人员的关注。尖峰计时可塑性(STDP)是一种无监督学习过程,它基于神经元突触前/后尖峰的相对计时,利用生物可塑性。与 STDP 相结合,SNN 的性能更佳,能耗更低。然而,由于 STDP 不会随监督而改变突触权重,因此很难确保突触权重始终收敛到能保证准确预测的值。为了解决这一限制,研究人员提出了一些机制,以诱导 STDP 参照当前的突触权重将突触权重收敛到适当的值上。因此,如果当前权重无法描述正确的突触连接,就无法诱导 STDP 正确更新突触权重。为了解决这个问题,我们提出了一种自适应机制,帮助 STDP 直接根据输入数据特征收敛突触权重:自适应突触模板(AST)。AST 根据数据特征来引导突触权重描述突触连接。它可以防止 STDP 根据无法描述正确突触连接的异常权重改变突触权重。这是因为它不会使用会干扰正确权重收敛的当前突触权重。我们将 AST 与 SNN 相结合,并进行了实验,将其与基线(不含 AST 的 SNN)和基准(以前改进 STDP 的工作)进行比较。实验结果表明,使用 AST 的 SNN 对各种数据集进行分类的准确率比基线和基准高 6%-39%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Synaptic Adjustment Mechanism to Improve Learning Performances of Spiking Neural Networks

Spiking Neural Networks (SNNs) are currently attracting researchers' attention due to their efficiencies in various tasks. Spike-timing-dependent plasticity (STDP) is an unsupervised learning process that utilizes bio-plausibility based on the relative timing of pre/post-synaptic spikes of neurons. Integrated with STDP, SNNs perform well consuming less energy. However, it is hard to ensure that synaptic weights always converge to values guaranteeing accurate prediction because STDP does not change synaptic weights with supervision. To address this limitation, researchers have proposed mechanisms for inducing STDP to converge synaptic weights on the proper values referring to current synaptic weights. Thus, if the current weights fail to describe proper synaptic connections, they cannot induce STDP to update synaptic weights properly. To solve this problem, we propose an adaptive mechanism that helps STDP to converge synaptic weights directly based on input data features: Adaptive synaptic template (AST). AST leads synaptic weights to describe synaptic connections according to the data features. It prevents STDP from changing synaptic weights based on abnormal weights that fail to describe the proper synaptic connections. This is because it does not use the current synaptic weights that can disturb proper weight convergence. We integrate AST with an SNN and conduct experiments to compare it with a baseline (the SNN without AST) and benchmarks (previous works to improve STDP). Our experimental results show that the SNN using AST classifies various data sets with 6%–39% higher accuracy than the baseline and benchmarks.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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