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.

神经元之间的复发相互作用会产生活性的动态模式，该模式用作底物 行为相关的计算。但是，我们还没有关联神经的原则框架 神经计算的动力学。我们最近综合了一个理论，该理论解释了级别的水平 复发性神经网络可以作为计算的基础。我们的总体目标是整合 通过行为和电生理学的洞察力在清醒中，行为猴子建立一个猴子 将神经动力学与神经计算有关的原则框架。该项目将从反向开始 在简单的计时任务中捕获皮质动力学的工程低级网络模型。然后我们移动 系统地朝着逐渐更高的等级网络模型，可以执行定时任务 逐渐更复杂的计算需求，例如时间间隔的概率推断。 我们旨在创建同时成功执行与任务相关的计算的模型（即 行为）并模拟执行这些任务的猴子中记录的皮质动力学。我们将使用这个 迭代过程，建立一个将神经动力学与神经计算有关的原则性框架 基础推理。最后，我们将使用此框架进行测试 空前的计算灵活性水平。 相关性（请参阅说明）： 越来越明显的是，健康和疾病行为的神经生物学必须是 探测神经元种群的水平。但是，我们尚无严格和定量的 将种群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生理学 通过神经网络建模并旨在通过动态数学来开发这种语言 系统。结果对未来的翻译研究有希望，以诊断 大脑功能障碍的计算模块和支持的动态模式 那些模块。