Elucidating Principles of Sensorimotor Control using Deep Learning
使用深度学习阐明感觉运动控制原理
基本信息
- 批准号:10488409
- 负责人:
- 金额:$ 2.79万
- 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:2022-09-15 至 2023-07-01
- 项目状态:已结题
- 来源:
- 关键词:AnatomyArtificial IntelligenceAutomobile DrivingBRAIN initiativeBehaviorBehavioralBiologicalBiophysicsBrainBrain regionCollaborationsCommunitiesComplexComputer ModelsComputer softwareDataDevelopmentDropsFeedbackFutureGenerationsGoalsInfrastructureJointsLaboratoriesLearningMachine LearningMeasurementMeasuresMethodsModelingMotorMovementMusMuscleMusculoskeletalMusculoskeletal SystemNervous system structureNeural Network SimulationNeuroanatomyNeuronsNeurosciencesOutcomeOutcomes ResearchPhysicsPsychological reinforcementResearchResourcesRestRodentRoleSignal TransductionSystemTechniquesTestingTheoretical modelVertebral columnbasebehavioral outcomebiomechanical modelcloud basedcomputational neurosciencecontrol theorydeep learningdesignexperimental studyhigh dimensionalitykinematicsmachine learning methodmotor controlmultidimensional dataneural circuitneural modelneural stimulationneuroregulationnonhuman primateprogramsrecurrent neural networkrelating to nervous systemtoolvirtual laboratory
项目摘要
Project Summary
How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This
understanding and characterization will be critical towards the application of principled neurostimulation to
specific brain regions to study the effect of neural circuit perturbations on behavior, and conversely towards
predictions of the neural activity during perturbations in the behavior. The research objective of this BRAIN
Initiative proposal is to develop biologically-inspired goal- and data- driven artificial intelligence methods to
elucidate the neurodynamical basis of sensorimotor control. The outcomes of this research program will
fundamentally impact our understanding of the neural circuits underlying sensorimotor control. The tools
developed herein will be disseminated for free use by the scientific community.
Through this BRAIN Initiative proposal, we will elucidate principles of sensorimotor control by incorporating
recorded neural data in succinct and interpretable biologically-inspired models of the relationships between the
measured biological data and the corresponding behavior. In Aims 1 and 2, we will design and disseminate a
comprehensive modeling framework that integrates large-scale neural and behavioral data with physics-based
modeling of the musculoskeletal system and neuroanatomical constraints. The neural data and neuroanatomical
constraints will be incorporated in recurrent neural network models that will achieve desired behavior through
biophysically-based musculoskeletal models using cutting-edge machine learning methods. The modeling
framework created here will provide a much-needed opportunity to design a virtual laboratory in Aim 3 that tests
the effect of neural stimulation in a feedback setting, and predicts the effect of unseen behavioral conditions and
behavioral perturbation on resulting neural activity.
Breakneck advances in hardware and machine learning techniques have led to vast improvements in our ability
to record and model large-scale multi-regional neural data. Our broad research goal is to advance the current
state-of-the-art for modeling the neural control of movements by incorporating large-scale measurements and
biological constraints into theoretical models of sensorimotor control. This is a critical step towards (a) elucidating
the computational role of neural activity from different brain regions in controlling complex behavior, (b) allowing
us to further refine theoretical models of movement generation based on data, and (c) understanding where and
how to stimulate the brain in order to efficiently apply neurostimulation for achieving desired behavior. With the
development and dissemination of these tools, we hope to enter an era where virtual laboratories are not just a
way to analyze previously performed experiments, but are integrated into experimental pipelines such that they
can be utilized to their full potential during large-scale neuroscience experiments.
项目摘要
分布式神经回路如何驱动复杂的肌肉骨骼系统进行有目的的运动?这
理解和表征对于原则性神经刺激的应用至关重要,
特定的大脑区域来研究神经回路扰动对行为的影响,反之亦然。
行为扰动期间神经活动的预测。这个大脑的研究目标
倡议建议是开发生物启发的目标和数据驱动的人工智能方法,
阐明感觉运动控制的神经动力学基础。该研究项目的成果将
从根本上影响了我们对感觉运动控制背后的神经回路的理解。的工具
将分发本报告所编写的材料,供科学界免费使用。
通过这个大脑倡议提案,我们将阐明感觉运动控制的原则,
在简洁和可解释的生物启发模型中记录神经数据,
测量的生物数据和相应的行为。在目标1和2中,我们将设计和传播一个
综合建模框架,将大规模神经和行为数据与基于物理的
肌肉骨骼系统和神经解剖学约束的建模。神经数据和神经解剖学
约束将被纳入递归神经网络模型,通过
基于生物医学的肌肉骨骼模型,使用尖端的机器学习方法。建模
这里创建的框架将提供一个急需的机会,在Aim 3中设计一个虚拟实验室,
神经刺激在反馈环境中的影响,并预测看不见的行为条件的影响,
行为干扰对神经活动的影响
硬件和机器学习技术的突破性进步使我们的能力得到了巨大的提高
记录和模拟大规模多区域神经数据。我们广泛的研究目标是推动当前
通过结合大规模的测量,
将生物学限制转化为感觉运动控制的理论模型。这是一个关键步骤,以(a)阐明
来自不同大脑区域的神经活动在控制复杂行为中的计算作用,(B)允许
我们进一步完善基于数据的运动生成的理论模型,以及(c)了解
如何刺激大脑以有效地应用神经刺激来实现期望的行为。与
通过这些工具的开发和传播,我们希望进入一个虚拟实验室不仅仅是一个
方法来分析先前执行的实验,但被集成到实验管道中,
可以在大规模的神经科学实验中充分发挥其潜力。
项目成果
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