可训练的参考尖峰通过监督学习改善 SNN 的时间信息处理能力

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zeyuan Wang;Luis Cruz
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引用次数: 0

摘要

尖峰神经网络(SNN)是下一代神经网络,由生物神经元组成,通过尖峰序列进行通信。通过修改尖峰神经网络的可塑性参数(包括权重和时间延迟),可以训练尖峰神经网络执行各种人工智能任务,但其性能一般无法与典型的人工神经网络(ANN)媲美。要提高 SNN 的性能,一种可能的解决方案是考虑从大脑神经系统固有的复杂性中提取权重和时间延迟以外的可塑参数,这可能有助于 SNN 提高其信息处理能力并实现类似大脑的功能。在此,我们提出将参考尖峰作为 SNNs 监督学习方案中的一种新型可塑性参数。神经元通过突触接收参考尖峰,在学习过程中提供独立于输入的参考信息。从理论上讲,参考尖峰通过在细节层面上调节输入尖峰的整合,可以改善 SNN 的时间信息处理。通过比较计算实验,我们利用监督学习证明,参考尖峰提高了 SNNs 将输入尖峰模式映射到目标输出尖峰模式的记忆能力,并提高了 MNIST、Fashion-MNIST 和 SHD 数据集的分类准确率,其中输入和目标输出都是时间编码的。我们的研究结果表明,应用参考尖峰可以提高 SNN 的时间信息处理能力,从而提高 SNN 的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning
Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be trained to perform various AI tasks, although in general not at the same level of performance as typical artificial neural networks (ANNs). One possible solution to improve the performance of SNNs is to consider plastic parameters other than just weights and time delays drawn from the inherent complexity of the neural system of the brain, which may help SNNs improve their information processing ability and achieve brainlike functions. Here, we propose reference spikes as a new type of plastic parameters in a supervised learning scheme in SNNs. A neuron receives reference spikes through synapses providing reference information independent of input to help during learning, whose number of spikes and timings are trainable by error backpropagation. Theoretically, reference spikes improve the temporal information processing of SNNs by modulating the integration of incoming spikes at a detailed level. Through comparative computational experiments, we demonstrate using supervised learning that reference spikes improve the memory capacity of SNNs to map input spike patterns to target output spike patterns and increase classification accuracy on the MNIST, Fashion-MNIST, and SHD data sets, where both input and target output are temporally encoded. Our results demonstrate that applying reference spikes improves the performance of SNNs by enhancing their temporal information processing ability.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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