Connectivity principles underlying network dynamics and learning

网络动态和学习的连接原理

• 批准号: 10507579
• 负责人: Vivek Athalye
• 金额: $ 12.54万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10507579
• 关键词: Behavior; Belief; Brain; Collaborations; Computer Models; Dimensions; Foundations; Future; Holography; Image; Individual; Institutes; Investigation; Knowledge; Lead; Learning; Measurement; Measures; Memory; Mentorship; Microscopy; Modeling; Modification; Motor Cortex; Movement; Mus; Neurobiology; Neurons; Neurosciences; Opsin; Optics; Organism; Outcome; Pattern; Performance; Population; Positioning Attribute; Protocols documentation; Psychological reinforcement; Recurrence; Research; Research Personnel; Running; Shapes; Techniques; Testing; Therapeutic; Time; Training; Universities; Visit; Work; auditory feedback; base; brain behavior; brain machine interface; calcium indicator; control theory; design; driving behavior; experimental study; in vivo; in vivo evaluation; innovation; learning network; network models; neural patterning; neuroprosthesis; next generation; novel; novel strategies; programs; relating to nervous system; response; skills; spatiotemporal; two photon microscopy

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.

项目摘要 如果一个有机体执行了一个导致期望结果的动作，它就能够在一段时间内再次执行该动作 的未来，以获得同样的结果。虽然强化学习机制的研究已经 广泛研究了大脑如何学习某些行为比其他行为更有价值，但知之甚少 关于大脑实际上如何按需重新进入神经状态以产生导致的行为 期望的结果。这是神经科学的一个核心问题，它是学习、记忆和认知的基础。 运动，并对恢复这些能力的疗法（包括脑机接口）具有影响。这是 相信神经元之间的连接会产生动态——大脑如何在之间转换的规则 神经状态——连接性的修改使得学习能够重新进入神经状态。然而，两个 主要的实验挑战阻碍了直接研究：1）测量和操纵连通性 体内神经元之间的关系，以及 2) 识别产生行为的神经元和活动模式。 在本提案中，我将使用 1) 2 光子显微镜来测量和克服这些挑战 通过光刺激单个目标神经元并测量 网络的响应，以及 2) 定义神经活动如何进行的脑机接口 (BMI) 范式 转化为行为和强化。通过实验应用这些技术 网络动力学的新颖模型，我的建议寻求功能连接如何的原则 是网络动态的基础，并支持运动皮层的学习，运动皮层是生成神经元的关键区域 移动。在第一个目标（K99）中，我将确定网络动力学模型是否预测功能 连接性以及图案化光刺激如何通过连接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。体重指数 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 还将通过测试是否可以确定功能连接的变化是否支持学习 光刺激更容易传播进入学习的网络状态。最后，在目标 3 (R00) 中，我将揭示 网络活动如何改变网络连接和动态的原则。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。 K99 期间，这项工作将在 Zuckerman 大脑与行为研究所合作进行 在哥伦比亚大学，在行动神经生物学专家 Rui Costa 博士和 Liam 博士的指导下 Paninski – 计算建模专家，与光学专家 Darcy Peterka 博士合作 和具有光刺激的 2 光子显微镜。我相信他们的技术和专业指导将 让我领导一个独立小组，研究网络如何生成和学习动态的原理 驾驶行为。这项工作将具有重要的治疗应用，包括脑机接口。