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

神经元之间的反复相互作用会产生动态的活动模式，作为 与行为相关的计算。然而，我们还没有一个将神经联系起来的原则性框架。 从动力学到神经计算。我们最近综合了一种理论，解释了低排名 递归神经网络可以作为计算的基础。我们的首要目标是整合 从这一理论中获得的洞察力与行为和电生理学在清醒、表现猴子方面建立了一个 将神经动力学与神经计算联系起来的原则性框架。这个项目将从相反的方向开始 设计低级别的网络模型，在简单的计时任务中捕获大脑皮层动力学。然后我们就搬家 系统地朝着可以执行计时任务的逐步更高级别的网络模型发展 逐渐更复杂的计算需求，例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)，并模拟在执行这些任务的猴子身上记录的皮质动力学。我们将使用这个 建立将神经动力学与神经计算联系起来的原则性框架的迭代过程 潜在的推论。最后，我们将使用一个新任务对此框架进行测试，该任务需要 前所未有的计算灵活性。 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 在神经元群体的水平上进行了探索。然而，我们还没有一个严谨和量化的 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电生理学 用神经网络建模，目的是通过动态数学来开发这样一种语言 系统。这一结果为未来的翻译研究诊断行为症状提供了希望 大脑功能障碍的计算模块和动态活动模式支持 那些模块。

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.