Connectivity Principles Underlying Network Dynamics and Learning

网络动态和学习的连接原理

基本信息

批准号：
10651856
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
项目类别：
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/10651856
关键词：
Behavior Belief Brain Collaborations Computer Models Dimensions Experimental Models Foundations Future Holography Image Individual 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 brain machine interface calcium indicator control theory design driving behavior experimental study in vivo in vivo evaluation innovation learning network network models neural neural patterning neuroprosthesis next generation novel novel strategies programs response skills spatiotemporal two photon microscopy

Behavior Belief Brain Collaborations Computer Models Dimensions Experimental Models Foundations Future Holography Image Individual 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 brain machine interface calcium indicator control theory design driving behavior experimental study in vivo in vivo evaluation innovation learning network network models neural neural patterning neuroprosthesis next generation novel novel strategies programs response skills spatiotemporal two photon microscopy

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项目摘要

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.

项目摘要如果有机体执行导致预期结果的动作，则能够再次执行该动作为了获得相同的结果，未来。而在强化学习机制方面的工作广泛研究大脑如何学习某些动作比其他动作更有价值的，几乎没有知识关于大脑实际上如何重新进入神经状态，以产生导致的行为期望的结果。这是神经科学中的一个核心问题，它是学习，记忆和运动，对疗法具有恢复这些能力，包括脑机界面。这是认为神经元之间的连通性产生了动态 - 符号的大脑过渡神经状态 - 连通性的修改使学习能够重新进入神经状态。但是，两个主要的实验挑战阻碍了直接投资：1）测量和操纵连通性在体内神经元之间，以及2）识别产生行为的神经元和活动模式。在此提案中，我将使用1）2光子显微镜来克服这些挑战，以测量和通过光刺激个体靶向神经元并测量体内操作功能连通性网络的响应和2）脑机界面（BMI）范式来定义神经活动的方式变成行为和加强。通过基于这些技术的实验网络动态的新型模型，我的建议寻求有关功能连接的原则基础网络动力学并在运动皮层中学习，这是生成的关键区域移动。在第一个目标（K99）中，我将确定网络动力学模型预测功能是否功能连通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AIM 2（K99/R00）中，我将设计一个BMI来研究功能连接是否限制了学习。 BMI 将测试可以通过光刺激传播输入的网络状态更容易。我还将确定功能连接性的变化是否通过测试是否是否光刺激更容易传播到进入学习的网络状态。最后，在AIM 3（R00）中，我将透露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，并揭示了改变网络的刺激方案的原理。在K99期间，这项工作将在Zuckerman大脑与行为协作研究所进行在哥伦比亚大学（Rui Costa）博士的指导下，在哥伦比亚大学（Columbia University） - 行动神经生物学专家和利亚姆（Liam）博士 Paninski - 计算建模专家，以及Darcy Peterka博士的合作 - 光学专家和2光子显微镜进行光刺激。我相信他们的技术和职业心态将会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则驾驶行为。这项工作将具有重要的治疗应用，包括用于脑机界面。