A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity
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DOI: 10.1371/journal.pcbi.1001110
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Cited by:
- Antonino Casile & Rose T Faghih & Emery N Brown, 2021. "Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-22, January.
- Matthieu Gilson & Ruben Moreno-Bote & Adrián Ponce-Alvarez & Petra Ritter & Gustavo Deco, 2016. "Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-30, March.
- Yu, Haitao & Guo, Xinmeng & Qin, Qing & Deng, Yun & Wang, Jiang & Liu, Jing & Cao, Yibin, 2017. "Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 674-687.
- Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
- Kris V Parag & Glenn Vinnicombe, 2017. "Point process analysis of noise in early invertebrate vision," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-25, October.
- Etesami, Jalal & Habibnia, Ali & Kiyavash, Negar, 2017. "Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity," LSE Research Online Documents on Economics 70769, London School of Economics and Political Science, LSE Library.
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