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Pulsewidth Modulation-Based Algorithm for Spike Phase Encoding and Decoding of Time-Dependent Analog Data

机译:基于脉冲脉冲调制的峰值算法和时间依赖性模拟数据的解码

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摘要

This article proposes a new spike encoding and decoding algorithm for analog data. The algorithm uses the pulsewidth modulation principles to achieve a high reconstruction accuracy of the signal, along with a high level of data compression. Two benchmark data sets are used to illustrate the method: stock index time series and human voice data. Applications of the method for spiking neural network (SNN) modeling and neuromorphic implementations are discussed. The proposed method would allow the development of new applications of SNNs as regression techniques for predictive time-series modeling.
机译:本文提出了一种用于模拟数据的新的Spike编码和解码算法。该算法使用脉冲宽度调制原理来实现信号的高重建精度,以及高水平的数据压缩。两个基准数据集用于说明方法:库存指数时间序列和人类语音数据。讨论了尖峰神经网络(SNN)建模和神经形态实施方法的应用。所提出的方法将允许开发SNN的新应用作为预测时间序列建模的回归技术。

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