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:10507579
- 负责人:
- 金额:$ 12.54万
- 依托单位:
- 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:
- 关键词:BehaviorBeliefBrainCollaborationsComputer ModelsDimensionsFoundationsFutureHolographyImageIndividualInstitutesInvestigationKnowledgeLeadLearningMeasurementMeasuresMemoryMentorshipMicroscopyModelingModificationMotor CortexMovementMusNeurobiologyNeuronsNeurosciencesOpsinOpticsOrganismOutcomePatternPerformancePopulationPositioning AttributeProtocols documentationPsychological reinforcementRecurrenceResearchResearch PersonnelRunningShapesTechniquesTestingTherapeuticTimeTrainingUniversitiesVisitWorkauditory feedbackbasebrain behaviorbrain machine interfacecalcium indicatorcontrol theorydesigndriving behaviorexperimental studyin vivoin vivo evaluationinnovationlearning networknetwork modelsneural patterningneuroprosthesisnext generationnovelnovel strategiesprogramsrelating to nervous systemresponseskillsspatiotemporaltwo 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
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Vivek Athalye其他文献
Vivek Athalye的其他文献
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{{ truncateString('Vivek Athalye', 18)}}的其他基金
Connectivity Principles Underlying Network Dynamics and Learning
网络动态和学习的连接原理
- 批准号:
10651856 - 财政年份:2022
- 资助金额:
$ 12.54万 - 项目类别:
Unraveling constraints on motor cortical activity exploration and shaping during structural skill learning using large-scale 2-photon imaging and holographic optogenetic stimulation
使用大规模 2 光子成像和全息光遗传学刺激,揭示结构技能学习过程中运动皮层活动探索和塑造的限制
- 批准号:
9788757 - 财政年份:2018
- 资助金额:
$ 12.54万 - 项目类别:
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