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).

摘要 跨越理论、心理物理学和生理学的广泛研究已经调查了我们如何依赖于统计数据。 在环境中学习，以改善我们的感觉运动行为：（1）贝叶斯理论提供了一个 理解如何利用统计学的优势，（2）心理物理实验 已经记录了这种行为的影响，（3）电生理学实验已经 识别出了反映这些信号的神经信号。一个重要的考虑因素是， 环境很少稳定。因此，一个最紧迫和未解决的问题，在这个前沿， 跨学科的工作是可塑的大脑信号如何通过经验逐渐获得信息 新的环境统计数据。在这里，我们将通过开发一种感觉运动行为来解决这个问题。 范式在非人类灵长类动物模型，需要自适应统计学习（目标1）。我们将使用这个 范式来测试特定的计算动机的假设，关于如何结构和动力学的 额叶皮层候选区域的神经活动在整个学习过程中发生变化（目标2）。最后，我们将使用 一个动态系统的方法来分析学习信号在额叶皮层的层状组织， 梳理出支持感觉运动学习的具有不同输入-输出特性的功能子回路（Aim （3）第三章。