Experimental Data from Sheikhattar et al. (2018) Extracting neuronal functional network dynamics via adaptive Granger causality analysis, Proceedings of the National Academy of Sciences (PNAS), 2018 (www.pnas.org/cgi/doi/10.1073/pnas.1718154115)

The deposited data sets contain:

1) Simulated spike trains from a network of interacting neurons
2) Two-photon calcium imaging data from the mouse auditory cortex (Kanold Lab, UMD)
3) Single-unit spike data from the ferret auditory and prefrontal cortices (Neural Systems Lab, UMD)

Please refer to readme.txt for further details. These data are used in the following article:

A. Sheikhattar, S. Miran, J. Liu, J. B. Fritz, S. A. Shamma, P. O. Kanold, and B. Babadi (2018). Extracting neuronal functional network dynamics via adaptive Granger causality analysis, Proceedings of the National Academy of Sciences (PNAS), 2018 (www.pnas.org/cgi/doi/10.1073/pnas.1718154115)

and are disseminated for public use in the spirit of easing reproducibility. The MATLAB implementation of the algorithms used in this work are deposited on Github at https://github.com/Arsha89/AGC Analysis.

本存档数据集包含以下内容：
1. 交互神经元网络模拟产生的锋电位序列（spike trains）
2. 小鼠听觉皮层的双光子钙成像（two-photon calcium imaging）数据（采集自马里兰大学Kanold实验室，UMD）
3. 雪貂听觉皮层与前额叶皮层的单神经元锋电位数据（single-unit spike data，采集自马里兰大学神经系统实验室，UMD）

详细信息请参阅readme.txt文件。本数据集已应用于以下研究论文：
A. Sheikhattar、S. Miran、J. Liu、J. B. Fritz、S. A. Shamma、P. O. Kanold 与 B. Babadi（2018）。论文题为《基于自适应格兰杰因果分析（adaptive Granger causality analysis）提取神经元功能网络动态特性》，发表于《美国国家科学院院刊》（Proceedings of the National Academy of Sciences，PNAS）2018年刊（链接：www.pnas.org/cgi/doi/10.1073/pnas.1718154115）。

本数据集以促进研究可重复性为宗旨，面向公众开放共享。本研究所用算法的MATLAB实现代码已上传至GitHub，仓库地址为https://github.com/Arsha89/AGC Analysis。