Weng Geyu, Clark Kelsey, Akbarian Amir, Noudoost Behrad, Nategh Neda
Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.
Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, United States.
Front Comput Neurosci. 2024 Jan 29;18:1273053. doi: 10.3389/fncom.2024.1273053. eCollection 2024.
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors' contributions to the representation and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.
为了创建视觉世界的行为相关表征，高等视觉区域的神经元表现出动态反应变化，以解释外部（如视觉输入）和内部（如奖励价值）因素之间随时间变化的相互作用。由此产生的高维表征空间给精确量化个体因素在行为过程中对感觉信息表征和读出的贡献带来了挑战。广泛使用的点过程广义线性模型（GLM）方法提供了一个强大的框架，用于在单试次和单个神经元水平上，将神经元处理定量描述为各种感觉和非感觉输入（编码）的函数，以及将特定反应成分与特定行为（解码）联系起来。然而，大多数现有的GLM变体都假设神经系统是时不变的，这使得它们不足以对高等视觉区域神经元敏感性的非平稳特征进行建模。在这篇综述中，我们总结了一些现有的GLM变体，重点是时变扩展。我们强调它们在理解高等视觉区域神经表征、解码瞬态神经元敏感性以及通过操纵模型组件将生理学与行为联系起来方面的应用。这类时变统计模型为高等视觉区域各种视觉行为的神经基础提供了有价值的见解，并在揭示支配大脑不同区域各种行为背后神经元处理的基本计算原则方面具有巨大潜力。
