Sensorimotor learning through adjustments of cortical dynamics

通过调节皮质动力学进行感觉运动学习

• 批准号: 10539258
• 负责人: Mehrdad Jazayeri
• 金额: $ 38.78万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10539258
• 关键词: Address; Anatomy; Animal Model; Animals; Area; Basal Ganglia; Bayesian Prediction; Behavior; Behavioral; Behavioral Paradigm; Brain; Cerebellum; Computer Models; Decision Theory; Dorsal; Electrophysiology (science); Environment; Exhibits; Foundations; Functional disorder; Human; Intervention; Language; Learning; Medial; Modeling; Monkeys; Motor; Neurobiology; Neurons; Output; Performance; Physiology; Population; Population Dynamics; Primates; Probability; Process; Property; Psychophysics; Reproduction; Research; Role; Shapes; Signal Transduction; Specificity; Structure; Testing; Thalamic structure; Time; Variant; behavioral study; dynamic system; experience; experimental study; frontal lobe; frontier; improved; neural; neural circuit; neuromechanism; neurotransmission; nonhuman primate; novel; novel therapeutic intervention; response; statistical learning; statistics; theories; time interval

Abstract Extensive research spanning theory, psychophysics, and physiology has investigated how we rely on statistical regularities in the environment to improve our sensorimotor behavior: (1) Bayesian theory has provided an understanding of how one should take advantage of statistical regularities, (2) psychophysical experiments have documented the impact of such regularities on behavior, and (3) electrophysiology experiments have identified neural signals that reflect those regularities. An important consideration is that statistical properties of the environment are rarely stable. Therefore, a most pressing and unresolved question at the frontier of this interdisciplinary body of work is how malleable brain signals, through experience, gradually acquire information about new environmental statistics. Here, we will tackle this problem by developing a sensorimotor behavioral paradigm in the non-human primate model that demands adaptive statistical learning (Aim 1). We will use this paradigm to test specific computationally-motivated hypotheses regarding how the structure and dynamics of neural activity in candidate regions of the frontal cortex change throughout learning (Aim 2). Finally, we will use a dynamical systems approach to analyze the laminar organization of learning signals in the frontal cortex to tease apart functional sub-circuits with distinct input-output properties that support sensorimotor learning (Aim 3).

抽象的 广泛的研究涵盖理论，心理物理学和生理学已经调查了我们如何依赖统计 环境中的规律以改善我们的感觉运动行为：（1）贝叶斯理论提供了一种 了解如何利用统计规律，（2）心理物理实验 已经记录了此类规律对行为的影响，（3）电生理实验具有 确定了反映这些规律性的神经信号。一个重要的考虑因素是 环境很少稳定。因此，在此的边界最紧迫，最不可解决的问题 跨学科的工作是通过经验逐渐获取信息的可延展大脑信号 关于新的环境统计。在这里，我们将通过开发感觉运动行为来解决这个问题 需要适应性统计学习的非人类灵长类动物模型中的范式（AIM 1）。我们将使用这个 范式测试特定计算动机的假设 整个学习过程中，额叶皮层的候选区域的神经活动（AIM 2）。最后，我们将使用 一种动态系统方法，用于分析额叶皮层中学习信号的层流组织 取笑功能子电路具有支持感觉运动学习的不同输入输出属性（AIM 3）。