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.

项目概要 分布式神经回路如何驱动复杂的肌肉骨骼系统有目的地运动？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BRAIN的研究目标 倡议提案是开发受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。该研究计划的成果将 从根本上影响我们对感觉运动控制神经回路的理解。工具 本文开发的内容将免费传播供科学界使用。 通过这个 BRAIN Initiative 提案，我们将通过结合以下内容来阐明感觉运动控制的原理： 将神经数据记录在简洁且可解释的生物启发模型中，描述神经元之间的关系 测量的生物数据和相应的行为。在目标 1 和 2 中，我们将设计并传播 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 约束将被纳入循环神经网络模型中，该模型将通过以下方式实现所需的行为 使用尖端机器学习方法的基于生物物理的肌肉骨骼模型。造型 此处创建的框架将为在 Aim 3 中设计一个测试虚拟实验室提供急需的机会 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和 对由此产生的神经活动的行为扰动。 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 记录和建模大规模多区域神经数据。我们广泛的研究目标是推进当前的 通过结合大规模测量和对运动的神经控制进行建模的最先进技术 将生物约束纳入感觉运动控制的理论模型中。这是朝着 (a) 阐明 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）了解在哪里和 如何刺激大脑以有效地应用神经刺激来实现所需的行为。随着 随着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 分析以前进行的实验的方法，但被集成到实验管道中，以便它们 可以在大规模神经科学实验中充分发挥其潜力。