A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks
A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks
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DOI：
10.1007/s11063-021-10562-2
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发表时间：
2021-07-22
影响因子：
3.1
通讯作者：
Knoll, Alois
中科院分区：
文献类型：
--
作者：
Auge, Daniel;Hille, Julian;Knoll, Alois
受生物启发的尖峰神经网络在人工智能领域越来越受欢迎，因为它们能够解决复杂问题，同时具有高能效。他们通过利用离散尖峰的时间作为主要信息载体来做到这一点。然而，仍然缺乏工业应用，部分原因是如何将传入数据编码为离散的尖峰事件的问题不能得到统一回答。在本文中，我们总结了文献中提出的信号编码方案，并提出了一个统一的命名法，以防止模糊定义的使用。因此，我们对编码方案的理论基础和应用进行了综述。这项工作为尖峰信号编码提供了基础，并概述了使用这些方案的不同面向应用的实现。
Biologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.