Efficient Hyperdimensional Computing With Spiking Phasors

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jeff Orchard;P. Michael Furlong;Kathryn Simone
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引用次数: 0

Abstract

Hyperdimensional (HD) computing (also referred to as vector symbolic architectures, VSAs) offers a method for encoding symbols into vectors, allowing for those symbols to be combined in different ways to form other vectors in the same vector space. The vectors and operators form a compositional algebra, such that composite vectors can be decomposed back to their constituent vectors. Many useful algorithms have implementations in HD computing, such as classification, spatial navigation, language modeling, and logic. In this letter, we propose a spiking implementation of Fourier holographic reduced representation (FHRR), one of the most versatile VSAs. The phase of each complex number of an FHRR vector is encoded as a spike time within a cycle. Neuron models derived from these spiking phasors can perform the requisite vector operations to implement an FHRR. We demonstrate the power and versatility of our spiking networks in a number of foundational problem domains, including symbol binding and unbinding, spatial representation, function representation, function integration, and memory (i.e., signal delay).
利用尖峰相位进行高效超维计算
超维(HD)计算(也称为向量符号架构,VSA)提供了一种将符号编码成向量的方法,允许这些符号以不同的方式组合成同一向量空间中的其他向量。向量和运算符构成了一个组合代数,因此复合向量可以分解回其组成向量。许多有用的算法都可以在高清计算中实现,如分类、空间导航、语言建模和逻辑。在这封信中,我们提出了傅立叶全息还原表示法(FHRR)的尖峰实施方案,这是最通用的 VSA 之一。FHRR 向量每个复数的相位被编码为一个周期内的尖峰时间。从这些尖峰相位衍生出来的神经元模型可以执行必要的向量运算,从而实现 FHRR。我们在多个基础问题领域展示了我们的尖峰网络的强大功能和多功能性,包括符号绑定和解绑、空间表示、函数表示、函数整合和记忆(即信号延迟)。
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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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