Sensorimotor learning through adjustments of cortical dynamics

摘要 横跨理论、心理物理学和生理学的广泛研究调查了我们是如何依赖统计学的 改善我们感觉运动行为的环境规则：(1)贝叶斯理论提供了一个 理解如何利用统计规律，(2)心理物理实验 记录了这种规律对行为的影响，以及(3)电生理学实验 识别出反映这些规律的神经信号。一个重要的考虑因素是 环境很少是稳定的。因此，在这个前沿，一个最紧迫和悬而未决的问题 跨学科的工作是如何延展性大脑信号，通过经验，逐渐获得信息 关于新的环境统计数据。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 非人灵长类动物模型中需要自适应统计学习的范例(目标1)。我们将使用这个 测试特定的由计算驱动的假设的范例，这些假设是关于 额叶皮质候选区域的神经活动在学习过程中会发生变化(目标2)。最后，我们将使用 用动力学系统方法分析额叶皮质学习信号的层状组织 拆分具有支持感觉运动学习(AIM)的不同输入输出属性的功能子电路 3)。

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).