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.

摘要 大多数知觉、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，区域之间的信号是 双向：较低的区域与较高的区域通过前馈连接进行通信，较高的区域发出信号 通过反馈降低到较低的区域。前馈通路被认为是日益复杂的 当一个人在视觉层次上向上攀登时，接受场。反馈信号在视觉处理中的作用 相比之下，人们对此知之甚少。已经提出了反馈来支持一组不同的相互关联的功能 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。事实证明，测试这些建议在实验上是困难的：它不仅需要评估什么信号 反馈信号是如何与正在进行的人群活动相互作用的 影响目标区域的前馈信号回传到更高的区域。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来支持视觉功能。我们 将通过确定神经元群体尖峰反应所传达的信号来实现这一点--这是 大脑皮层表示--在前馈和反馈方向。我们将使用高产量的多区域神经元 录音；如何实施区域间信号传递的新概念框架；以及新的分析工具 这将使我们能够理清前馈、循环和反馈信号的影响，即使在 它们同时处于活动状态。我们的工作假设是前馈-反馈循环实现了一种形式的 预测编码，到目前为止，这个概念主要是使用单个神经元的反应进行测试，而不是 人口信号的分级流动。在目标1中，我们将通过同时分析来检验这一假设 在猕猴V1/V2和V1/V4中记录的神经元群体反应，由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在我们的实验中测量的响应联系起来，并 了解在数据中观察到的区域间信号模式如何有助于(或约束)预测 编码计算。在目标3中，我们将测试动物如何通过感知做出积极的预测 决策任务，在大脑皮层区域之间传递，形成视觉大脑皮层表征。我们的 雄心勃勃的目标将通过汇集三个绩效指标的互补专业知识来实现，建立在 建立和成功的合作。该项目的顺利完成将使国际上的研究发生转移。 从单个神经元到基于群体的相互作用的网络信号发送，并将测试 神经科学。分层预测编码。我们期待着我们获得的理解，以及分析和 我们开发的概念性工具将广泛适用。因为区域间信号在 几种疾病，我们的发现也可能为未来工作中开发治疗方法奠定基础。