Institute of Computing Science, Faculty of Computing and Telecommunications, Poznan University of Technology, Piotrowo 3A Street, 61-138 Poznań, Poland.
Sensors (Basel). 2021 May 10;21(9):3276. doi: 10.3390/s21093276.
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.
本文描述了一种基于边缘计算的用于时间波形分析的尖峰神经网络 (SNN) 的架构。该网络模型基于在间脑预处理信号的原理，并使用典型的丘脑区域的紧张性尖峰和抑制诱导的尖峰模型。研究的重点是通过消除大多数突触连接并确保神经元层之间的连接的权重值零分散，显著降低 SNN 算法的复杂性。本文描述了一种网络映射和学习算法，其中学习过程中的变量数量与模式的大小呈线性相关。该工作包括测试各种网络大小的准确性参数的稳定性。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，这是安培技术的典型特征。一个实际应用的例子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作为半导体结构实现的成本。
