Identification of interacting neural populations: methods and statistical considerations

Author:

Kass Robert E.123ORCID,Bong Heejong3,Olarinre Motolani13,Xin Qi23,Urban Konrad N.23ORCID

Affiliation:

1. Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States

2. Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States

3. Department of Statistics & Data Science, 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.

Funder

National Institute of Mental Health and Neurosciences

Publisher

American Physiological Society

Subject

Physiology,General Neuroscience

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