Visualization of Neuron Data using Nonlinear Technic

2019 International SoC Design Conference (ISOCC) Pub Date : 2019-10-01 DOI:10.1109/ISOCC47750.2019.9027753

Y. Uwate, Y. Nishio, M. Obien, U. Frey

引用次数: 0

Abstract

In our previous study, we have proposed the method to use nonlinear time-series analysis to apply to neuronal data for visualizing a characteristic of neurons. We set up three types of neuron data which are observed at different days. By applying three nonlinear time-series analysis, we confirmed that the youngest neuron has strong activity and the neuronal behavior settles down as the day goes on. In this study, we investigate the effect of the delay parameter of attractor reconstruction of nonlinear time-series analysis. From observed results, we can see that the appropriate value of delay parameter exists to display the network characteristics.

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使用非线性技术的神经元数据可视化

在我们之前的研究中，我们提出了将非线性时间序列分析应用于神经元数据的方法来可视化神经元的特征。我们设置了三种类型的神经元数据，在不同的日子观察。通过三个非线性时间序列分析，我们证实了最年轻的神经元具有很强的活动，神经元的行为随着时间的推移而稳定下来。本文研究了非线性时间序列分析中延时参数对吸引子重构的影响。从观察结果可以看出，存在合适的延迟参数值来显示网络特性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 International SoC Design Conference (ISOCC)

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