Sensorimotor learning through adjustments of cortical dynamics

通过调节皮质动力学进行感觉运动学习

基本信息

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
项目类别：
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

项目摘要

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).

抽象的涵盖理论、心理物理学和生理学的广泛研究调查了我们如何依赖统计数据环境中的规律性可以改善我们的感觉运动行为：（1）贝叶斯理论提供了一个了解如何利用统计规律，（2）心理物理学实验已经记录了这种规律对行为的影响，并且（3）电生理学实验已经识别出反映这些规律的神经信号。一个重要的考虑因素是统计特性环境很少是稳定的。因此，这一领域最紧迫且尚未解决的问题跨学科的工作主体是可塑性大脑如何通过经验发出信号，逐渐获取信息关于新的环境统计数据。在这里，我们将通过开发感觉运动行为来解决这个问题非人类灵长类动物模型中的范式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驱动的假设，这些假设涉及结构和动力学如何额叶皮层候选区域的神经活动在学习过程中发生变化（目标 2）。最后，我们将使用一种动态系统方法来分析额叶皮层学习信号的层状组织梳理具有不同输入输出属性的功能子电路，支持感觉运动学习（目标 3）。