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预期结果的动作，它就能够再次执行该动作。 为了达到同样的结果。虽然关于强化学习机制的工作 广泛研究了大脑如何学习某些行为比其他行为更有价值，但几乎没有知识 关于大脑如何实际上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 运动，并对恢复这些能力的治疗有影响，包括脑机接口。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-并且连接的修改使得学习能够重新进入神经状态。然而两 主要的实验挑战阻碍了直接的研究：1）测量和操纵连接 以及2）识别产生行为的神经元和活动模式。 在这个建议中，我将克服这些挑战，使用1）2光子显微镜测量， 通过光刺激单个靶向神经元并测量神经元的功能， 网络的反应，以及2）脑机接口（BMI）范式，以定义神经活动是如何 转化为行为和强化。通过应用这些技术的实验， 网络动力学的新模型，我的建议寻求的原则，如何功能连接 是网络动力学的基础，并使运动皮层能够学习，运动皮层是生成 运动在第一个目标（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/R 00）中，我将设计一个BMI来研究功能连接是否会限制学习。的BMI 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的变化是否支持学习来确定功能连接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（R 00）中，我将揭示 网络活动如何改变网络连接和动态的原则。我会测试不同的协议 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Zuckerman大脑和行为研究所进行 在哥伦比亚大学的指导下，Rui Costa博士是行动神经生物学专家， Paninski -计算建模专家，并与Darcy Peterka博士合作-光学专家 和双光子显微镜与光刺激。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有重要的治疗应用，包括脑机接口。