Journal article
A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data
IEEE/ACM transactions on computational biology and bioinformatics, v 15(2), pp 562-569
01 Mar 2018
PMID: 29610104
Abstract
In systems neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure the causal interactions among multiple neurons in the brain is crucial to understanding how neurons work in concert to generate specific brain functions. The development of new statistical methods for assessing causal influence between spike trains is still an active field of neuroscience research. Here, we suggest a copula-based Granger causality measure for the analysis of neural spike train data. This method is built upon our recent work on copula Granger causality for the analysis of continuous-valued time series by extending it to point-process neural spike train data. The proposed method is therefore able to reveal nonlinear and high-order causality in the spike trains while retaining all the computational advantages such as model-free, efficient estimation, and variability assessment of Granger causality. The performance of our algorithm can be further boosted with time-reversed data. Our method performed well on extensive simulations, and was then demonstrated on neural activity simultaneously recorded from primary visual cortex of a monkey performing a contour detection task.
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Details
- Title
- A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data
- Creators
- Meng Hu - Drexel UniversityWu Li - Beijing Normal UniversityHualou Liang - Drexel University
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 15(2), pp 562-569
- Publisher
- IEEE
- Number of pages
- 8
- Grant note
- 2014CB846101 / National Key Basic Research Program of China; National Basic Research Program of China 31125014 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000428936900024
- Scopus ID
- 2-s2.0-85044949144
- Other Identifier
- 991019168354704721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability