Real-time statistical algorithms for controlling neural dynamics and behavior

项目概要/摘要 高通量实验神经科学使观察许多人的行为成为可能 动物，以及一大群神经元同时，提供了一个令人兴奋的机会 弄清楚神经系统如何执行感知、认知和认知基础的计算 行为。然而，数据分析和数据的科学循环存在重大瓶颈。 由于噪声、高维数据的复杂性和规模，收集。主要目标 该项目旨在开发用于跟踪大脑内部状态的工具，这些工具不直接 可通过行为和神经信号进行测量，并生成最佳刺激 对应当前大脑状态。这些外部刺激可以用来扰乱动物的 使动物表现不同的关于世界的信念或策略。 目标 1：我们的团队将开发一个神经状态跟踪系统，该系统将解析并显示复杂的 从动物大脑实时记录的神经信号。神经状态跟踪算法将 还提取神经系统运行的规律，使神经科学家能够生成新的 关于智能行为背后的人口水平实现的一类假设。 目标 2：因果检验关于神经元群体如何计算并产生有意义的结果的假设 行为，有必要能够扰乱内部计算过程。我们将开发一个 反馈控制系统通过新颖的控制在短时间内扰动神经动力学 尊重大脑自身自由度的神经计算方案。 目标 3：通过理解和跟踪整个学习过程中内部策略的时间演变，我们 可以学习如何优化动物行为的训练。为了这个目标，我们将开发统计数据 学习模型和计算系统，根据过去生成最佳刺激 动物的表现。 该项目开发的统计工具可能会加速以下领域的基本发现： 神经科学。在临床上，这项研究可以扩展到监测、诊断和构建下一代 生成针对神经动力学或行为障碍的实时反馈刺激装置 帕金森病、自闭症、学习障碍、强迫症等 和癫痫。

Project Summary / Abstract High-throughput experimental neuroscience has made it possible to observe behavior of many animals, as well as a large groups of neurons simultaneously, providing an exciting opportunity for figuring out how the neural system performs computations that underlie perception, cognition, and behavior. However, there is a major bottleneck in the scientific cycle of data analysis and data collection due to the complexity and scale of noisy, high-dimensional data. The primary objective of this project is to develop tools for tracking the internal state of the brain that are not directly measurable from both the behavior and neural signals, and to generate optimal stimulus corresponding to the current brain state. These external stimuli can be used to perturb the animal’s belief or strategy about the world such that the animal would behave differently. Aim 1: Our team will develop a neural state tracking system that will parse out and display complex neural signals recorded from the animal brain in real-time. The neural state tracking algorithm will also extract the law that the neural system operates under, allowing neuroscientist to generate a new class of hypotheses about the population level implementation underlying intelligent behavior. Aim 2: To causally test hypothesis on how population of neurons compute and produce meaningful behavior, it is necessary to be able to perturb the internal computation process. We will develop a feedback control system to perturb the neural dynamics at a short time scale with a novel control scheme for neural computation that respects the brain’s own degrees of freedom. Aim 3: By understanding and tracking the time evolution of internal strategy throughout learning, we can learn how to optimize the training of animal behavior. In this aim, we will develop statistical models of learning and a computational system to generate the best stimuli based on the past performance of the animal. The statistical tools developed in this project will likely accelerate fundamental discoveries in neuroscience. Clinically, this research can extend to monitoring, diagnosing, and building next- generation real-time feedback stimulation devices for disorders with a neurodynamic or behavioral component such as Parkinson’s disease, autism, learning disorders, obsessive compulsive disorder, and epilepsy.