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Updated: Jul 23, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
Identification of interacting neural populations: methods and statistical considerations
Robert E Kass1,2,3, Heejong Bong3, Motolani Olarinre1,3
1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States.
Abstract:
As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.
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