Information coding in neurons: using systems-identification to link cellular, circuit and behavioral neurophysiology

神经元中的信息编码：利用系统识别将细胞、回路和行为神经生理学联系起来

• 批准号: RGPIN-2018-04877
• 负责人: Cook, Erik
• 金额: $ 2.11万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=648020
• 关键词: Information; coding; neurons; using; systems

OBJECTIVE: The long-term objective of our laboratory's research program is to link the activity of neurons to our higher-level perceptual experiences. To understand how neuronal activity is linked to our perception of the external world, we need to know the mathematical rules neurons use to encode information in their action potential (or spike) output. This is usually referred to as understanding the “neural code”. A major unanswered question is how do the nonlinearities in single neurons and correlations between many neurons shape the neural code?******APPROACH: We propose to study the neural code at three levels. The first level of investigation addresses the nonlinearities of single neurons at the cellular level using intracellular recordings. The second level addresses how the intact neural circuit of the retina influences the nonlinearities of single retinal ganglion cells. For the third level, we will use extracellular electrophysiological recordings from the visual cortex to link correlations in neuronal activity to visually-guided behaviors.******The goal of this research grant is to link these three separate levels of investigation (cellular, circuit and systems) using quantitative systems-identification approaches. To do this, we will study neural coding at each level using similar experimental design, stimuli and analysis methods. The benefit of using a systems-identification approach is that it allows us to describe the neural code using a common set of functional tools such as linear filters and static nonlinearities. By having a common description of the the cellular, circuit and behavioral neurophysiology, we are more likely to find a common computational framework******SPECIFIC AIMS: The overall goal is to create functional models of our cellular, circuit and systems neurophysiological data. The data for this proposal will be collected in collaboration with our partners and in our lab. We have a large library of both intra- and extracellular neuronal recordings and behaviors to work with. The challenge has been to find the right quantitative models to reveal the neural code at each level.******Aim 1: Determine the cellular-level rules and nonlinearities that dendrites use to integrate multiple inputs.***Aim 2: Determine the circuit-level rules and nonlinearities that retinal ganglion cells use to integrate visual and optogenetically-delivered inputs. ***Aim 3: Determine the behavioral-level rules that the visual cortex uses to integrate correlated visual inputs.******Specifically, we will focus on the role of nonlinearities and neural correlations.******IMPACT: Using functional models to link cellular, circuit and behavioral neurophysiology will provide us with a mathematically-based understanding of how neurons process information, which is critical for our larger goal of revealing how neural circuits in the brain underlie perception and behavior.

目的：我们实验室研究项目的长期目标是将神经元的活动与我们更高层次的感知体验联系起来。为了理解神经元活动是如何与我们对外部世界的感知联系在一起的，我们需要知道神经元用于在其动作电位（或峰值）输出中编码信息的数学规则。这通常被称为理解“神经密码”。一个悬而未决的主要问题是，单个神经元的非线性和许多神经元之间的相关性是如何形成神经密码的？******方法：我们建议在三个层次上研究神经编码。第一级的研究解决了非线性的单个神经元在细胞水平上使用细胞内记录。第二层论述了完整的视网膜神经回路如何影响单个视网膜神经节细胞的非线性。对于第三个层次，我们将使用来自视觉皮层的细胞外电生理记录来将神经元活动与视觉引导行为的相关性联系起来。******这项研究资助的目标是使用定量系统识别方法将这三个独立的研究水平（细胞，电路和系统）联系起来。为此，我们将使用类似的实验设计、刺激和分析方法来研究每个层次的神经编码。使用系统识别方法的好处是，它允许我们使用一组通用的功能工具（如线性滤波器和静态非线性）来描述神经代码。通过对细胞，电路和行为神经生理学的共同描述，我们更有可能找到一个共同的计算框架******具体目标：总体目标是创建我们的细胞，电路和系统神经生理学数据的功能模型。本提案的数据将与我们的合作伙伴和我们的实验室合作收集。我们有一个庞大的细胞内和细胞外神经元记录和行为库。挑战在于找到正确的定量模型来揭示每个层次的神经密码。******目标1：确定树突用于整合多个输入的细胞级规则和非线性。***目的2：确定视网膜神经节细胞用于整合视觉和光遗传传递输入的电路水平规则和非线性。***目标3：确定视觉皮层用于整合相关视觉输入的行为水平规则。******具体来说，我们将关注非线性和神经相关性的作用。******影响：使用功能模型将细胞、电路和行为神经生理学联系起来，将为我们提供基于数学的神经元如何处理信息的理解，这对于揭示大脑中的神经回路如何构成感知和行为的更大目标至关重要。