论文标题
从神经元细胞记录的高维数据的连通性估计
Connectivity estimation of high dimensional data recorded from neuronal cells
论文作者
论文摘要
本文的主要结果是开发了一种新型的连接估计方法，称为总尖峰概率边缘（TSPE）。基于在不同时间尺度的互相关和边缘过滤基于此方法，并在这项工作中概述了理论框架。 TSPE通过使用记录的动作电位来实现抑制性和兴奋性连接之间的分类。为了将有关最新算法状态的方法进行比较，需要估计连通性。经过一项研究，在UAS Aschaffenburg的BioMems实验室中，对进一步的研究主题进行了有希望的算法。由于已知的连通性，使用了在计算机网络中评估这些算法的。这可以验证我们算法结果的正确性。因此，首先需要具有生物物理代表性的神经元网络模拟。数据集以不同的方式进行模拟，并进行了分析以开发一个评估框架。经过在计算机网络中成功进行评估之后，体外实验及其分析完成了该项目。
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.
