Shaping Neural Population Dynamics to Facilitate Learning

项目摘要 行为和认知来自于相互作用的群体的协调活动， 神经元为了改变行为，我们必须改变驱动网络的架构。 行为神经元群体的活动在运动过程中发生变化的规律是什么？ 学习？如果我们能观察到技能学习背后的神经群体动态， 也许能促进学习。这种能力最终可能会改善神经- 基于康复策略，可以促进中风的恢复。 我们将使用神经反馈范例来塑造神经群体动态， 是技能学习的基础。为了做到这一点，我们将记录运动神经元的活动， 恒河猴受试者的皮质。动物会通过以下方式控制电脑屏幕上的光标： 产生神经指令信号这是神经反馈，因为动物直接 观察他的神经活动的投射，以光标移动的形式。 这种范式允许我们通过简单地扰乱神经活动的映射来研究学习 到屏幕上的光标移动。在这样的扰动之后，动物必须发现 以产生新的神经活动模式，现在适合恢复良好的控制， 光标。 我们将提出两个相互关联的问题。首先，动物是如何学会产生新的 神经活动的模式第二，我们能否推动这一进程？基于神经反馈 学习为研究神经群体提供了手臂运动的互补优势 伴随学习的动力学。只有在神经反馈范例中，我们才能 可以肯定的是，我们直接观察到的学习引起的神经活动变化， 行为，因为只有我们记录的神经元直接影响这种范式中的行为。 我们将测试技能学习的经典理论，转化为关于以下方面的具体假设： 神经活动模式将如何在多日的技能学习过程中发生变化。 如果我们的基于神经反馈的增量训练方案确实促进了学习， 这项研究将为中风患者的康复专家提供信息。我们（和 其他人）认为，如果基于神经反馈的疗法与标准的行为疗法相结合， 中风的治疗，康复结果会改善。

Project Summary Behavior and cognition emerge from the coordinated activity of populations of interacting neurons. To change behavior we must change the architecture of the network that drives behavior. What are the rules whereby the activity of populations of neurons can change during learning? If we could observe the neural population dynamics that underlie skill learning, we might be able to facilitate learning. This ability may eventually lead to improvements to neurally- based rehabilitation strategies that can facilitate the recovery from stroke. We will use a neurofeedback paradigm to shape the neural population dynamics that underlie skill learning. To do this, we will record the activity of dozens of neurons in the motor cortex of Rhesus monkey subjects. Animals will control a cursor on a computer screen by generating neural command signals. This is neurofeedback because the animal directly observes a projection of his neural activity, in the form of the movement of the onscreen cursor. This paradigm allows us to study learning simply by perturbing the mapping from neural activity to cursor movement on the screen. Following such a perturbation the animal must discover how to generate new patterns of neural activity that are now appropriate to restore good control of the onscreen cursor. We will ask two linked questions. First, how does the animal learn to generate new patterns of neural activity? And second, can we facilitate that process? Neurofeedback-based learning offers complementary advantages to arm movements for studying the neural population dynamics that accompany learning. Chiefly, only in a neurofeedback paradigm can we be certain that the learning-induced changes in neural activity we observe matter directly for behavior, because only the neurons we record directly impact behavior in this paradigm. We will test classic theories of skill learning, converted into specific hypotheses about how neural activity patterns will change throughout the multi-day course of skill learning. If our neurofeedback-based incremental training schemes do facilitate learning, then this research will inform the work of rehabilitation specialists who work with stroke patients. We (and others) believe that if neurofeedback-based therapies are coupled with standard behavioral therapies for stroke, rehabilitation outcomes will improve.