神经元细胞高维数据的连通性估计
Connectivity estimation of high dimensional data recorded from neuronal cells
摘要：
本论文的主要成果是发展了一种新的连通性估计方法，称为总尖峰概率边缘（TSPE）。基于互相关和边缘滤波在不同的时间尺度上提出了这种方法，并概述了理论框架，在这项工作中。TSPE能够通过使用记录的动作电位在抑制性连接和兴奋性连接之间进行分类。为了比较这种方法，学习最先进的算法来估计连通性是必要的。经过研究，有前途的算法被实施和评估，用于进一步的研究课题，其中包括在UAS Aschaffenburg的生物实验室。为了评估这些算法，使用Silico网络，因为它们已知的连通性。这使得它可以验证我们的算法结果的正确性。因此，首先需要一个具有生物学代表性的神经元网络模拟。以不同方式模拟和分析了数据集，以制定评价框架。在通过计算机网络进行成功评估后，体外实验及其分析完成了该项目。
英文摘要：
The main result of this thesis is the development of a novel connectivity estimation method, called Total Spiking Probability Edges (TSPE). Based on cross-correlation and edge filtering at different time scales this method is proposed and the theoretical framework is outlined in this work. TSPE enables the classification between inhibitory and excitatory connections by using recorded action potentials. To compare this method learning about state of the art algorithms to estimate connectivity is necessary. After a research, promising algorithms are implemented and evaluated for further research topics, among others in the biomems lab of UAS Aschaffenburg. To evaluate these algorithms in silico networks are used, because of their known connectivity. This makes it possible to validate the correctness of our algorithm results. Therefore, a biophysically representative neuronal network simulation is needed first. Datasets were simulated in different ways and analysed in order to develop an evaluation framework. After a successful evaluation with in silico networks, in vitro experiments and their analyses complete this project.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
