Connectivity principles underlying network dynamics and learning

项目摘要 如果一个有机体执行了一个导致预期结果的动作，它能够在 为了获得同样的结果，我们必须在未来取得同样的成果。而关于强化学习机制的工作有 广泛研究大脑如何学习某些动作比其他动作更有价值，但知之甚少 关于大脑实际上是如何按需重新进入神经状态以产生导致 想要的结果。这是神经科学中的一个中心问题，它是学习、记忆和 并对恢复包括脑机接口在内的这些能力的治疗方法产生了影响。它是 认为神经元之间的连接产生了动力学--大脑如何在 神经状态--这种连通性的改变使学习能够重新进入神经状态。然而，有两个 主要的实验挑战阻碍了直接调查：1)测量和操纵连通性 在活体神经元之间，以及2)识别产生行为的神经元和活动模式。 在这项提议中，我将克服这些挑战，使用1)双光子显微镜来测量和 通过光刺激单个靶神经元并测量 网络的响应，以及2)定义神经活动如何的脑机接口(BMI)范例 转化为行为和强化。通过应用这些技术的实验，基于 网络动力学的新模型，我的提案寻求如何实现功能连接的原则 作为网络动力学的基础，在运动皮质中实现学习，运动皮质是产生 有动静。在第一个目标(K99)中，我将确定网络动力学模型是否预测泛函 连通性以及图案化光刺激如何通过连通性传播以修改网络状态。 在目标2(K99/R00)中，我将设计一个BMI来研究功能连接是否限制学习。BMI指数 将测试是否更容易学习可以通过光刺激传播进入的网络状态。我 还将通过测试功能连接性的变化是否支持学习 光刺激更容易传播以进入学习的网络状态。最后，在目标3(R00)中，我将揭示 有关网络活动如何改变网络连通性和动态的原则。我将测试不同的协议 用于刺激时空模式，并揭示改变网络的刺激协议的原理。 在K99期间，这项工作将在合作的扎克曼大脑和行为研究所进行 在哥伦比亚大学，在鲁伊·科斯塔博士-行动神经生物学专家和利亚姆博士的指导下 Paninski-计算建模专家，并与Darcy Peterka博士-光学专家合作 和具有光刺激的双光子显微镜。我相信他们的技术和专业指导将 让我领导一个独立的小组，研究网络如何产生和学习动态的原理 驾驶行为。这项工作将具有重要的治疗应用，包括脑机接口。

PROJECT ABSTRACT If an organism performs an action that leads to a desired outcome, it is able to perform that action again in the future in order to obtain that same outcome. While work on the mechanisms of reinforcement learning has extensively studied how the brain learns certain actions are more valuable than others, there is little knowledge about how the brain actually re-enters neural states on-demand to produce the behavior that leads to the desired outcome. This is a central question in neuroscience which underlies learning, memory, and movement and has implications for therapies to restore these abilities including brain-machine interfaces. It is believed that connectivity between neurons gives rise to dynamics—rules for how the brain transitions between neural states—and that modification of connectivity enables learning to re-enter neural states. However, two main experimental challenges have impeded direct investigation: 1) measuring and manipulating connectivity between neurons in vivo, and 2) identifying the neurons and activity patterns generating a behavior. In this proposal, I will overcome these challenges using 1) 2-photon microscopy to measure and manipulate functional connectivity in vivo by photostimulating individual targeted neurons and measuring the network’s response, and 2) a brain-machine interface (BMI) paradigm to define how neural activity is transformed into behavior and reinforcement. Through experiments that apply these techniques based on novel models of network dynamics, my proposal seeks principles for how functional connectivity underlies network dynamics and enables learning in motor cortex, a critical region for generating movement. In the first Aim (K99), I will determine whether a model of network dynamics predicts functional connectivity and how patterned photostimulation propagates through connectivity to modify the network state. In Aim 2 (K99/R00), I will design a BMI to study whether functional connectivity constrains learning. The BMI will test whether it is easier to learn network states that can be entered through photostimulation propagation. I will also determine whether changes in functional connectivity support learning by testing whether photostimulation more easily propagates to enter learned network states. Finally, in Aim 3 (R00), I will reveal principles for how network activity can change network connectivity and dynamics. I will test different protocols for stimulating spatiotemporal patterns and reveal principles of stimulation protocols that change the network. During the K99, this work will be conducted in the collaborative Zuckerman Institute for Brain and Behavior at Columbia University with the mentorship of Dr. Rui Costa - expert in the neurobiology of action and Dr. Liam Paninski – expert in computational modeling, and with the collaboration of Dr. Darcy Peterka – expert in optics and 2-photon microscopy with photostimulation. I believe their technical and professional mentorship will position me to lead an independent group studying principles for how networks generate and learn dynamics driving behavior. This work will have important therapeutic applications, including for brain-machine interfaces.