Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States.
J Neurophysiol. 2023 Sep 1;130(3):475-496. doi: 10.1152/jn.00131.2023. Epub 2023 Jul 19.
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.
随着记录技术的改进为神经生理学研究创造了新的机会，研究重点已经从单个神经元转移到形成回路的多个神经元群体，因此提供跨群体协调活动的证据变得尤为重要。我们回顾了各种实现这一目标的方法，将它们分为六大类，避免了技术描述，而是专注于高级别的动机和关注点。我们的目的是指出这些方法可以实现的目标，以及它们可能成功的情况。为此，我们讨论了四个交叉问题：神经元群体的定义、试验到试验的可变性和泊松样噪声、时变动态和因果关系。
