Real-time statistical algorithms for controlling neural dynamics and behavior

项目摘要/摘要 高通量的实验神经科学使观察许多 动物，以及一大群神经元同时存在，为 弄清楚神经系统如何执行构成感知、认知和 行为。然而，在数据分析和数据的科学循环中存在一个主要瓶颈 由于噪声、高维数据的复杂性和规模，收集。的主要目标是 这个项目是为了开发跟踪大脑内部状态的工具，这些工具不是直接的 从行为和神经信号都可以测量，并产生最佳刺激 与当前的大脑状态相对应。这些外部刺激可以用来扰乱动物的 关于世界的信仰或策略，使动物的行为有所不同。 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情况 从动物大脑中实时记录的神经信号。神经状态跟踪算法将 还提取了神经系统运行所遵循的规律，允许神经科学家产生新的 一类关于智能行为背后的种群水平实现的假设。 目标2：因果检验关于神经元群体如何计算和产生有意义的假设 行为，有必要能够扰乱内部计算过程。我们将开发一种 一种新型的短时间尺度扰动神经动力学的反馈控制系统 尊重大脑自身自由度的神经计算方案。 目标3：通过了解和跟踪内部策略在整个学习过程中的时间演变，我们 可以学习如何优化动物行为的训练。在这个目标中，我们将发展统计 学习模型和基于过去产生最佳刺激的计算系统 动物的表演。 在这个项目中开发的统计工具可能会加速在 神经科学。在临床上，这项研究可以扩展到监测、诊断和建立下一步- 用于神经动力学或行为障碍的生成实时反馈刺激装置 如帕金森氏症、自闭症、学习障碍、强迫症、 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.