The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

中文翻译：

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线性计算复杂度的点状神经网络用于电流分析中的波形分析

本文介绍了使用边缘计算对时间波形进行分析的尖峰神经网络（SNN）的体系结构。该网络模型基于预处理中脑中信号的原理，并使用丘脑区域典型的补品加标和抑制诱发的加标模型。该研究的重点是通过消除大多数突触连接并确保有关神经元层之间连接的权重值零分散，来显着降低SNN算法的复杂性。本文描述了一种网络映射和学习算法，其中学习过程中变量的数量线性依赖于模式的大小。这项工作包括测试各种网络规模的精度参数的稳定性。所描述的方法利用了尖峰神经元处理小于100 pA的电流的能力，这是电流分析技术的典型特征。实际应用的一个示例是使用基于碳纳米管（CNT）传感器的安培系统分析囊泡融合信号。本文最后讨论了将网络实现为半导体结构的成本。