Elucidating Principles of Sensorimotor Control using Deep Learning

使用深度学习阐明感觉运动控制原理

• 批准号: 10488409
• 负责人: Shreya Saxena
• 金额: $ 2.79万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10488409
• 关键词: Anatomy; Artificial Intelligence; Automobile Driving; BRAIN initiative; Behavior; Behavioral; Biological; Biophysics; Brain; Brain region; Collaborations; Communities; Complex; Computer Models; Computer software; Data; Development; Drops; Feedback; Future; Generations; Goals; Infrastructure; Joints; Laboratories; Learning; Machine Learning; Measurement; Measures; Methods; Modeling; Motor; Movement; Mus; Muscle; Musculoskeletal; Musculoskeletal System; Nervous system structure; Neural Network Simulation; Neuroanatomy; Neurons; Neurosciences; Outcome; Outcomes Research; Physics; Psychological reinforcement; Research; Resources; Rest; Rodent; Role; Signal Transduction; System; Techniques; Testing; Theoretical model; Vertebral column; base; behavioral outcome; biomechanical model; cloud based; computational neuroscience; control theory; deep learning; design; experimental study; high dimensionality; kinematics; machine learning method; motor control; multidimensional data; neural circuit; neural model; neural stimulation; neuroregulation; nonhuman primate; programs; recurrent neural network; relating to nervous system; tool; virtual laboratory

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从复杂的肌肉骨骼系统中驱动有目的的运动？这 理解和表征对于应用原则神经刺激的应用至关重要 特定的大脑区域研究神经回路扰动对行为的影响，相反 行为扰动期间神经活动的预测。这个大脑的研究目标 主动提案是开发以生物学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。该研究计划的结果将 从根本上影响了我们对感觉运动控制基础神经回路的理解。工具 本文开发的科学界将免费使用。 通过这项大脑倡议提案，我们将通过合并来阐明感觉运动控制原理 在简洁而可解释的生物学启发的模型中记录了神经数据的关系 测量的生物学数据和相应的行为。在目标1和2中，我们将设计和传播 全面的建模框架将大规模神经和行为数据与基于物理的数据集成 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 约束将纳入经常性的神经网络模型中，这些模型将通过 使用尖端机器学习方法的基于生物物理的肌肉骨骼模型。建模 在此创建的框架将提供急需的机会来设计AIM 3的虚拟实验室，以测试 反馈环境中神经刺激的影响，并预测看不见的行为条件的影响 导致神经活动的行为扰动。 硬件和机器学习技术的突破性进步导致了我们的能力 记录和建模大规模多区域神经数据。我们广泛的研究目标是促进当前 通过合并大规模测量和 对感官控制的理论模型的生物约束。这是（a）阐明的关键一步 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 我们要根据数据进一步完善运动生成的理论模型，以及（c）了解何处和 如何刺激大脑以有效地应用神经刺激以实现所需的行为。与 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时代 分析先前进行的实验的方法，但已集成到实验管道中，以便它们 在大规模的神经科学实验中，可以充分利用它们的潜力。