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SpikE: spike-based embeddings for multi-relational graph data
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2021-04-27 , DOI: arxiv-2104.13398
Dominik Dold, Josep Soler Garrido

Despite the recent success of reconciling spike-based coding with the error backpropagation algorithm, spiking neural networks are still mostly applied to tasks stemming from sensory processing, operating on traditional data structures like visual or auditory data. A rich data representation that finds wide application in industry and research is the so-called knowledge graph - a graph-based structure where entities are depicted as nodes and relations between them as edges. Complex systems like molecules, social networks and industrial factory systems can be described using the common language of knowledge graphs, allowing the usage of graph embedding algorithms to make context-aware predictions in these information-packed environments. We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations. Learning such spike-based embeddings only requires knowledge about spike times and spike time differences, compatible with recently proposed frameworks for training spiking neural networks. The presented model is easily mapped to current neuromorphic hardware systems and thereby moves inference on knowledge graphs into a domain where these architectures thrive, unlocking a promising industrial application area for this technology.

中文翻译:

SpikE:用于多关系图数据的基于峰值的嵌入

尽管最近成功实现了将基于尖峰的编码与错误反向传播算法相协调,但尖峰神经网络仍主要应用于由感官处理产生的任务,这些任务在视觉或听觉数据等传统数据结构上运行。可以在工业和研究领域得到广泛应用的丰富数据表示形式就是所谓的知识图-一种基于图的结构,其中实体被描述为节点,而它们之间的关系被描述为边缘。可以使用知识图的通用语言来描述诸如分子,社交网络和工业工厂系统之类的复杂系统,从而允许使用图嵌入算法在这些信息密集的环境中进行上下文感知的预测。我们提出了一种基于峰值的算法,其中图中的节点由神经元种群的单个峰值时间和种群之间的峰值时间差的关系表示。学习这样的基于尖峰的嵌入仅需要有关尖峰时间和尖峰时间差的知识,就可以与最近提出的用于训练尖峰神经网络的框架兼容。提出的模型可以轻松地映射到当前的神经形态硬件系统,从而将对知识图的推断移入这些体系结构蓬勃发展的领域,从而为该技术解锁有希望的工业应用领域。与最近提出的用于训练尖峰神经网络的框架兼容。提出的模型可以轻松地映射到当前的神经形态硬件系统,从而将对知识图的推断移入这些体系结构蓬勃发展的领域,从而为该技术解锁有希望的工业应用领域。与最近提出的用于训练尖峰神经网络的框架兼容。提出的模型可以轻松地映射到当前的神经形态硬件系统,从而将对知识图的推断移入这些体系结构蓬勃发展的领域,从而为该技术解锁有希望的工业应用领域。
更新日期:2021-04-29
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