Advanced Correlation Grid: Analysis and Visualisation of Functional Connectivity among Multiple Spike Trains
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 896
- Type
- D - Journal article
- DOI
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10.1016/j.jneumeth.2017.05.016
- Title of journal
- Journal of Neuroscience Methods
- Article number
- -
- First page
- 78
- Volume
- 286
- Issue
- -
- ISSN
- 0165-0270
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Finding the functional connectivity from simultaneously recorded spike trains is an important but difficult problem. Our new method takes into account the most significant peak of the cross-correlation function and the corresponding time shift to calculate a correlation grid of directed connections. This method has the advantage that it can automatically distinguish between direct functional connectivity and spurious connections such as common source and indirect connectivity. We apply cluster analysis to generate the connectivity diagram from the correlation grid. Multiple examples of data analysis (for model generated data and experimental recordings) are provided to demonstrate efficiency of this new method.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -