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

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未来为了获得同样的结果，强化学习机制的工作已经完成。一般研究大脑如何学习某些动作比其他动作更有价值，知识很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这是神经科学的一个核心问题，它是学习、记忆和学习的基础。运动，并对恢复这些能力的疗法（包括脑机接口）具有影响。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——并且连接性的修改使得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) 范式通过应用这些技术的实验转化为行为和强化。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力学的基础，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。将测试是否更容易学习可以通过光刺激传播输入的网络状态。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最后，在目标 3 (R00) 中，我将揭示光刺激更容易传播以进入学习的网络状态。关于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 博士合作和带有光刺激的 2 光子显微镜，我相信他们的技术和专业指导会。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这项工作将具有重要的治疗应用，包括脑机接口。