She Qi, So Winnie K Y, Chan Rosa H M
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2506-9. doi: 10.1109/EMBC.2015.7318901.
As the amount of experimental data made publicly accessible has gradually increased in recent years, it is now possible to reconsider many of the longstanding questions in neuroscience. In this paper, we present an efficient frame-work for reconstructing the functional connectivity from the spike train data curated from the Collaborative Research in Computational Neuroscience (CRCNS) program. We used a modified generalized linear model (GLM) framework with L1 norm penalty to investigate 10 datasets. These datasets contain spike train data collected from the hippocampal region of rats performing various tasks. Analysis of the reconstructed network showed that the neural network in the hippocampal region of well-trained rats demonstrated significant small-world features.
近年来，随着公开可用的实验数据量逐渐增加，现在有可能重新审视神经科学中许多长期存在的问题。在本文中，我们提出了一种有效的框架，用于从计算神经科学合作研究（CRCNS）计划策划的尖峰序列数据中重建功能连接。我们使用了带有L1范数惩罚的改进广义线性模型（GLM）框架来研究10个数据集。这些数据集包含从执行各种任务的大鼠海马区域收集的尖峰序列数据。对重建网络的分析表明，训练有素的大鼠海马区域的神经网络表现出显著的小世界特征。
