CRCNS: US-French Research Proposal: Principles of Inference through Neural Dynamics

CRCNS：美法研究提案：通过神经动力学进行推理的原理

• 批准号: 10178116
• 负责人: Mehrdad Jazayeri
• 金额: $ 18.47万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-19 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10178116
• 关键词: Area; Bayesian Analysis; Bayesian Prediction; Behavior; Behavioral; Behavioral Symptoms; Belief; Brain; Complex; Coupling; Data; Diagnosis; Dimensions; Disease; Electrophysiology (science); Engineering; Foundations; Future; Goals; Health; Instruction; Language; Link; Low Prevalence; Mathematics; Measurement; Modeling; Monkeys; Neural Network Simulation; Neurobiology; Neurons; Pattern; Population; Primates; Process; Production; Recurrence; Reproduction; Research Proposals; Series; Shapes; Silicon Dioxide; Structure; System; Testing; Training; Translational Research; Uncertainty; Work; awake; brain dysfunction; dynamic system; experimental study; flexibility; frontal lobe; high dimensionality; in vivo; insight; network models; non-linear transformation; novel; recurrent neural network; relating to nervous system; success; theories; time interval

Recurrent interactions between neurons generate dynamic patterns of activity that serve as a substrate for behaviorally relevant computations. However, we do not yet have a principled framework for relating neural dynamics to neural computations. We have recently synthesized a theory that explains how low-rank recurrent neural networks may serve as a building block for computations. Our overarching goal is to integrate insights from this theory with behavior and electrophysiology in awake, behaving monkeys to establish a principled framework relating neural dynamics to neural computations. The project will start with reverse engineering low-rank network models that capture cortical dynamics in simple timing tasks. We then move systematically toward progressively higher rank network models that can perform timing tasks with progressively more sophisticated computational demands such as probabilistic inference of time intervals. We aim to create models that simultaneously succeed in performing task-relevant computations (i.e., behavior) and emulate cortical dynamics recorded in monkeys performing those tasks. We will use this iterative process to establish a principled framework relating neural dynamics to neural computations underlying inference. Finally, we will put this framework to test using a novel task that demands an unprecedented level of computational flexibility. RELEVANCE (See instructions): It has become increasingly apparent that the neurobiology of behavior in health and disease has to be probed at the level of populations of neurons. However, we do not yet have a rigorous and quantitative language for linking population neural activity to behavior. Our work combines primate electrophysiology with neural network modeling and aims to develop such a language through the mathematics of dynamical systems. The results hold promise for future translational research to diagnose behavioral symptoms of brain dysfunction in terms of their computational modules and the dynamic patterns of activity that support those modules.

神经元之间的反复相互作用产生动态的活动模式，这些活动模式可以作为神经元之间相互作用的基础。 行为相关的计算。然而，我们还没有一个原则性的框架， 动力学到神经计算我们最近综合了一个理论，解释了低级别 递归神经网络可以用作计算的构建块。我们的首要目标是整合 从这一理论的见解与行为和电生理学在清醒，行为猴子建立一个 将神经动力学与神经计算联系起来的原则框架。该项目将从反向开始 设计低等级网络模型，在简单的计时任务中捕获皮层动力学。然后我们移动 系统地向更高等级的网络模型发展，可以执行定时任务， 逐渐地更复杂的计算需求，诸如时间间隔的概率推断。 我们的目标是创建同时成功执行任务相关计算的模型（即， 行为），并模仿执行这些任务的猴子记录的皮质动力学。我们将使用这个 建立将神经动力学与神经计算相关联的原则框架的迭代过程 基本推理最后，我们将使用一个新的任务来测试这个框架， 前所未有的计算灵活性。 相关性（参见说明）： 越来越明显的是，健康和疾病中行为的神经生物学必须是 在神经元群体的水平上探测。然而，我们还没有一个严格的和定量的 将群体神经活动与行为联系起来的语言。我们的工作结合了灵长类动物的电生理学 神经网络建模，旨在通过动态数学来开发这样一种语言。 系统.这些结果为未来的转化研究提供了希望，以诊断行为症状， 大脑的计算模块和动态的活动模式， 这些模块。