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: 9916986
• 负责人: Mehrdad Jazayeri
• 金额: $ 21.94万
• 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/9916986
• 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，作为基础 行为相关的计算。然而，我们还没有一个将神经网络联系起来的原则框架。 神经计算的动力学。我们最近综合了一个理论，解释了低秩是如何 循环神经网络可以作为计算的构建块。我们的首要目标是整合 从这一理论中得出的见解，结合清醒、有行为能力的猴子的行为和电生理学，建立了一个 将神经动力学与神经计算相关的原则框架。该项目将从反向开始 设计低阶网络模型，捕捉简单计时任务中的皮质动态。然后我们移动 系统地逐步走向更高等级的网络模型，可以执行计时任务 逐渐复杂的计算需求，例如时间间隔的概率推理。 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）并模拟执行这些任务的猴子记录的皮质动态。我们将使用这个 建立将神经动力学与神经计算联系起来的原则框架的迭代过程 底层推论。最后，我们将使用一个新颖的任务来测试这个框架，该任务需要 前所未有的计算灵活性。 相关性（参见说明）： 越来越明显的是，健康和疾病行为的神经生物学必须 在神经元群体水平上进行探索。然而，我们还没有一个严格的、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电生理学 神经网络建模，旨在通过动态数学开发这样一种语言 系统。这些结果为未来诊断行为症状的转化研究带来了希望 大脑功能障碍的计算模块和支持的动态活动模式 那些模块。