Understanding feedforward and feedback signaling between neuronal populations

了解神经元群体之间的前馈和反馈信号

• 批准号: 10446820
• 负责人: ADAM KOHN
• 金额: $ 199.96万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10446820
• 关键词: Anatomy; Animals; Area; Attention; Back; Brain; Code; Cognitive; Collaborations; Communication; Computer Models; Data; Decision Making; Disease; Feedback; Future; Goals; Learning; Macaca; Measures; Mental disorders; Modeling; Motor; Neurons; Neurosciences; Pathway interactions; Pattern; Population; Recurrence; Role; Schizophrenia; Shapes; Signal Transduction; Stimulus; Structure; System; Task Performances; Testing; Training; Vision; Visual; Visual Cortex; Visual system structure; Work; analytical method; analytical tool; area V1; area V4; autism spectrum disorder; base; expectation; experimental study; extrastriate visual cortex; network models; population based; predictive modeling; receptive field; response; theories; tool; visual processing; visual stimulus

Summary Most perceptual, cognitive, and motor functions rely on neuronal activity distributed across multiple networks, often located in different brain areas. In many systems, including the visual system, signaling between areas is bidirectional: lower areas communicate with higher ones via feedforward connections, and higher areas signal to lower areas via feedback. Feedforward pathways are thought to underlie the increasingly sophisticated receptive fields as one ascends the visual hierarchy. The role of feedback signaling in visual processing, in contrast, is poorly understood. Feedback has been proposed to underlie a diverse set of interrelated functions including providing contextual information, predictions, learning signals, and attentional and expectation signals. Testing these proposals has proven experimentally difficult: it requires assessing not only what signals are sent from higher to lower cortex but also how feedback signals interact with ongoing population activity in the target area to influence the feedforward signals relayed back to higher areas. In this project we aim to understand how inter-areal feedforward and feedback signaling work together to underlie visual function. We will do so by determining the signals conveyed by neuronal population spiking responses—which underlie cortical representation—in the feedforward and feedback direction. We will use high yield multi-area neuronal recordings; a new conceptual framework of how inter-areal signaling is implemented; and new analytical tools that will allow us to disentangle the influence of feedforward, recurrent, and feedback signaling, even when these are concurrently active. Our working hypothesis is feedforward-feedback loops implement a form of predictive coding, a concept that to date has been tested primarily using single neuron responses rather than the hierarchical flow of population signals. In Aim 1, we will test this hypothesis by analyzing simultaneously recorded neuronal population responses evoked in macaque V1/V2 and V1/V4, by a broad but targeted set of visual stimuli. In Aim 2, we will develop a hierarchical spiking network model of predictive coding. The model will allow us to relate existing theoretical constructs to the responses measured in our experiments and to understand how the pattern of inter-areal signaling observed in data contributes to (or constrains) predictive coding computation. In Aim 3, we will test how active predictions, made by animals performing a perceptual decision-making task, are relayed between cortical areas and shape visual cortical representations. Our ambitious goals will be accomplished by pooling the complementary expertise of three PIs, building on an established and successful collaboration. Successful completion of this project will shift the study of inter- network signaling from single neuron to population-based interactions and will test a central concept in neuroscience—hierarchical predictive coding. We expect the understanding we gain, and the analytic and conceptual tools we develop, will be broadly applicable. Because inter-areal signaling is dysregulated in several disorders, our findings may also lay the groundwork for developing treatments in future work.

总结 大多数感知、认知和运动功能依赖于分布在多个网络中的神经元活动， 通常位于不同的大脑区域。在许多系统中，包括视觉系统，区域之间的信号传递是 双向：较低区域通过前馈连接与较高区域通信，较高区域发出信号 通过反馈来降低区域。前馈通路被认为是越来越复杂的 感受野的变化。反馈信号在视觉处理中的作用， 对比之下，人们对此知之甚少。反馈被认为是一系列相互关联的功能的基础 包括提供上下文信息、预测、学习信号以及注意力和期望 信号.测试这些提议在实验上被证明是困难的：它不仅需要评估哪些信号， 从更高的皮层发送到更低的皮层，以及反馈信号如何与正在进行的群体活动相互作用， 目标区域影响被中继回更高区域的前馈信号。在这个项目中，我们的目标是 了解区域间前馈和反馈信号如何共同作用于视觉功能。我们 将通过确定由神经元群体尖峰响应传递的信号来实现， 皮层代表-在前馈和反馈方向。我们将使用高产量的多区域神经元 记录;关于如何实施区域间信号的新概念框架;以及新的分析工具 这将使我们能够解开前馈，经常性和反馈信号的影响，即使当 这些是同时有效的。我们的工作假设是前馈反馈回路实现了一种形式的 预测编码，迄今为止，这一概念主要使用单神经元反应进行测试，而不是 人口信号的等级流动。在目标1中，我们将通过同时分析 记录猕猴V1/V2和V1/V4中诱发的神经元群体反应，通过一组广泛但有针对性的 视觉刺激在目标2中，我们将开发预测编码的分层尖峰网络模型。模型 将使我们能够将现有的理论结构与我们实验中测量的反应联系起来， 了解数据中观察到的区域间信号模式如何有助于（或限制）预测 编码计算在目标3中，我们将测试动物进行知觉预测时， 决策任务，在皮层区域之间传递，并形成视觉皮层表征。我们 将通过汇集三个PI的互补专业知识来实现雄心勃勃的目标， 建立和成功的合作。该项目的成功完成将使跨- 网络信号从单个神经元到基于群体的相互作用，并将测试一个中心概念， 神经科学层次预测编码。我们期待我们获得的理解，以及分析和 我们开发的概念工具将广泛适用。由于区域间信号传导失调， 虽然我们的研究结果可能会导致多种疾病，但我们的研究结果也可能为未来的治疗方法奠定基础。