Detecting cell assemblies by NMF-based clustering from calcium imaging data
Detecting cell assemblies by NMF-based clustering from calcium imaging data
复制标题
通过基于 NMF 的钙成像数据聚类检测细胞组装体
DOI：
10.1016/j.neunet.2022.01.023
复制
发表时间：
2022
期刊：
影响因子：
7.8
通讯作者：
Murata Noboru
中科院分区：
文献类型：
--
作者：
Nagayama Mizuo;Aritake Toshimitsu;Hino Hideitsu;Kanda Takeshi;Miyazaki Takehiro;Yanagisawa Masashi;Akaho Shotaro;Murata Noboru
大量神经元形成细胞组件，在大脑中处理信息。最近测量技术的发展，其中之一是钙成像，使研究细胞组件成为可能。在这项研究中，我们的目标是从钙成像数据中提取细胞组件。提出了一种基于非负矩阵分解的聚类方法。该方法首先通过NMF得到神经元之间的相似度矩阵，然后对其进行谱聚类。NMF的应用带来了模型选择的问题。NMF中碱基的数目对结果有很大影响，目前还没有建立一种合适的选择方法。我们试图通过使用基于NMF的新定义的估计量进行模型平均来解决这个问题。在模拟数据上的实验表明，在较宽的采样率范围内，该方法优于传统的基于相关性的聚类方法。我们还分析了睡眠/清醒小鼠的钙成像数据，结果表明，细胞集合的大小取决于大脑皮层慢波产生的程度和空间范围。
A large number of neurons form cell assemblies that process information in the brain. Recent developments in measurement technology, one of which is calcium imaging, have made it possible to study cell assemblies. In this study, we aim to extract cell assemblies from calcium imaging data. We propose a clustering approach based on non-negative matrix factorization (NMF). The proposed approach first obtains a similarity matrix between neurons by NMF and then performs spectral clustering on it. The application of NMF entails the problem of model selection. The number of bases in NMF affects the result considerably, and a suitable selection method is yet to be established. We attempt to resolve this problem by model averaging with a newly defined estimator based on NMF. Experiments on simulated data suggest that the proposed approach is superior to conventional correlation-based clustering methods over a wide range of sampling rates. We also analyzed calcium imaging data of sleeping/waking mice and the results suggest that the size of the cell assembly depends on the degree and spatial extent of slow wave generation in the cerebral cortex.
登录
查看更多内容
影响因子：
48
作者：
Demas J;Manley J;Tejera F;Barber K;Kim H;Traub FM;Chen B;Vaziri A
通讯作者：
Vaziri A
影响因子：
5.4
作者：
Mölter J;Avitan L;Goodhill GJ
通讯作者：
Goodhill GJ
影响因子：
64.8
作者：
Lee, DD;Seung, HS
通讯作者：
Seung, HS
影响因子：
2.5
作者：
Massimini, M;Amzica, F
通讯作者：
Amzica, F