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、认知和运动功能依赖于分布在多个网络中的神经活动， 通常位于不同的大脑区域中，包括视觉系统，区域之间的信号传导是不同的。 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 通过反馈到达较低区域被认为是日益复杂的基础。 感受域作为视觉层次的提升，反馈信号在视觉处理中的作用。 相比之下，反馈被认为是一组不同的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 事实证明，测试这些建议在实验上很困难：它不仅需要评估哪些信号。 从较高皮层发送到较低皮层，还包括反馈信号如何与持续的群体活动相互作用 目标区域影响转发回更高区域的前馈信号。 我们了解区域间前馈和反馈信号如何协同工作以构成视觉功能。 将通过确定神经群体尖峰反应传达的信号来做到这一点——这是 皮质表示——在前馈和反馈方向，我们将使用高产量的多区域神经网络。 记录；如何实施区域间信号传输的新概念框架以及新的分析工具； 这将使我们能够消除前馈、循环和反馈信号的影响，即使在 我们的工作假设是前馈-反馈循环实现了一种形式。 预测编码，迄今为止主要使用单一神经反应而不是 在目标 1 中，我们将通过同时分析来检验这一假设。 记录了猕猴 V1/V2 和 V1/V4 中由一组广泛但有针对性的神经群体引起的反应 在目标 2 中，我们将开发一个预测编码的分层尖峰网络模型。 将使我们能够将现有的理论结构与我们的实验中测量的响应联系起来，并 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 在目标 3 中，我们将测试动物如何进行感知的主动预测。 决策任务在皮质区域之间传递并塑造我们的视觉皮质表征。 雄心勃勃的目标将通过汇集三位 PI 的互补专业知识来实现​​， 该项目的成功完成将改变国际间的研究。 从单个神经元到基于群体的交互的网络信号传递，并将测试一个中心概念 神经科学——分层预测编码。我们期望获得理解，以及分析和预测。 我们开发的概念工具将广泛适用，因为区域间信号传导失调。 对于多种疾病，我们的发现也可能为未来工作中开发治疗方法奠定基础。