Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, [6] propose a method for inferring effective connectivity networks from multi-electrode array data. In this paper, a novel statistical method called spectral mirror estimation [2] is applied to a time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks. A classical change point algorithm is then applied to this representation, which successfully detects a neuroscientifically significant change point coinciding with the time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically [9]. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.
翻译:为了更好地了解这些有机体的网络结构,[6]提出了一种从多电极阵列数据推断有效连接网络的方法。在本文中,一种称为光谱镜估计[2]的新型统计方法被应用于一系列时间序列的推断有效连通性有机体网络。这种方法生成了这些网络时间序列动态的一维等离光镜表示法。然后对这个表示法应用了一个古典变化点算法,成功地检测出一个与开始出现的时间抑制性神经元同时发生的神经科学重大变化点,以及天体细胞的百分比急剧上升[9]。这一发现表明,将等光镜动态结构发现法用于推断有机网络的有效连通时间序列的潜在效用。</s>