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.

概括 大多数感知，认知和运动功能都取决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，区域之间的信号是 双向：较低的区域通过前馈连接与较高的区域交流，较高的区域信号 通过反馈来降低区域。饲养途径被认为是越来越复杂的基础 接收领域作为一个视觉层次结构。反馈信号在视觉处理中的作用，在 对比，知之甚少。已经提出了反馈以支持各种相互关联的功能 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。测试这些建议在实验上很困难：它不仅需要评估哪些信号 从较高的皮质发送到较低的皮质，但反馈信号如何与正在进行的人口活动相互作用 影响前馈信号的目标区域转移回了更高的区域。在这个项目中，我们的目标是 了解暂时的前馈和反馈信号如何共同起作用以构成视觉功能的基础。我们 将通过确定神经元人口峰值反应传达的信号来做到这一点，这是基于的 皮质表示 - 在进料和反馈方向上。我们将使用高产量多区域神经元 录音；一个新的概念框架是如何实施美际信号；和新的分析工具 即使在 这些是同时活跃的。我们的工作假设是FeedForward反馈回路实施了一种形式 预测编码，迄今为止已经主要使用单个神经元响应而​​不是测试的概念 人口信号的层次流。在AIM 1中，我们将通过同时分析该假设来检验该假设 猕猴V1/V2和V1/V4中唤起的神经元种群反应是通过广泛但有针对性的 视觉刺激。在AIM 2中，我们将开发一个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的实验中测得的响应联系起来以及 了解在数据中观察到的全面信号传导模式如何有助于（或约束）预测 编码计算。在AIM 3中，我们将测试动物进行感知的主动预测 决策任务是在皮质区域之间和塑造视觉皮层表示之间的中继。我们的 雄心勃勃的目标将通过汇总三个PI的完整专业知识来实现​​，并以 建立并成功的合作。成功完成该项目将改变研究的研究 网络信号从单个神经元到基于人群的互动，将测试中心概念 神经科学 - 层次预测编码。我们期望我们获得的理解以及分析性和 我们开发的概念工具将广泛适用。因为暂停间信号在 几种疾病，我们的发现也可能为在未来工作中开发治疗方面奠定了基础。