The Virtual Rodent: A Platform to Study the Artificial and Biological Control of Natural Behavior

虚拟啮齿动物：研究自然行为的人工和生物控制的平台

• 批准号: 10633144
• 负责人: Diego Etiony Aldarondo
• 金额: $ 3.65万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 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/10633144
• 关键词: 3-Dimensional; Acceleration; Adaptive Behaviors; Address; Algorithms; Animal Behavior; Animals; Artificial Intelligence; Basal Ganglia; Behavior; Behavioral; Biological; Biomechanics; Biophysics; Brain; Brain Stem; Complex; Computer software; Consumption; Corpus striatum structure; Development; Disease; Engineering; Environment; Exhibits; Health; Human; Impairment; Learning; Link; Logic; Mammals; Manuals; Measures; Methods; Modeling; Motor; Movement; Muscle; Neurons; Neurosciences; Neurosciences Research; Paralysed; Pattern; Performance; Peripheral; Persons; Phase; Physics; Protocols documentation; Rattus; Regulation; Research; Rodent; Rodent Model; Sensory; Spinal Cord; Structure; System; Testing; Thalamic structure; Training; Walking; Work; analog; artificial neural network; behavior measurement; biological systems; deep reinforcement learning; experience; flexibility; in silico; in vivo; innovation; insight; interest; kinematics; mind control; motor control; motor learning; neural; neural circuit; neural model; neural network; neural prosthesis; neuroregulation; next generation; novel; sensory feedback; sensory system; skills; task analysis; tool; virtual

Project Summary Controlling complex bodies in uncertain environments is a challenge our brains have evolved to perfect, yet the algorithms and neural network implementations that enable flexible and robust control have been difficult to identify. This proposal is premised on the idea that progress will be served by embracing the complexities of the underlying control systems, including the bodies they control and the diversity of animal behavior. To test this idea and, more generally, provide a versatile platform for interrogating the neural circuit-level principles and mechanisms underlying embodied motor control, I propose the virtual rodent. This in-silico animal will have a body like a real rat, experience normal physics, and be trained to produce naturalistic rat behaviors. It will have an artificial brain that can be fully interrogated, manipulated, and reconfigured. After establishing this platform, I will develop an analysis approach to compare in-vivo neural activity from freely moving animals to the network representations of the model. This endeavor expands upon recent approaches linking neural representations with the representations of task-optimized artificial models in sensory systems, enabling the comparison of neural activity with analytical models in the motor domain and during complex behavior. I then propose to further develop the virtual rodent to probe questions related to hierarchical control and motor learning in animals and machines. In the F99 phase of this proposed research, I will continue to develop the virtual rodent as a platform to study the artificial and biological control of natural behavior. Specifically, in Aim 1, I will finalize a behavioral measurement, processing, and modeling pipeline to train artificial neural networks to imitate the behaviors of real rodents while in a physical simulator, validate its performance, and demonstrate its utility as a model for embodied motor control. In Aim 2, I will then record from motor centers of real rodents as they freely move and compare their neural activity to the network activity of models enacting the same diverse movements. In the K00 phase of this proposed research, I will expand upon the virtual rodent model to study hierarchical control, a conserved feature of flexible and adaptive mammalian control. I will train an artificial neural network to reuse lower-level control modules created as part of the F99 phase to autonomously solve motor tasks commonly used in motor neuroscience research. This Aim is of great value to the field of motor neuroscience as it will facilitate the comparison of neural activity of animals performing controlled tasks with the network activity of analytical models performing physically simulated analogues of the same tasks. Together, these Aims offer a new path in the study of the neural control of movement, one which embraces the complexity of behavior and biomechanics to advance our understanding of flexible and adaptive motor control in health and disease.

项目摘要 在不确定环境中控制复杂的身体是我们的大脑已经发展为完美的挑战，但是 难以灵活和稳健控制的算法和神经网络实现很难 确认。该提议的前提是，要通过拥抱复杂性来取得进展的想法 潜在的控制系统，包括他们控制的身体和动物行为的多样性。测试这个 想法，更普遍地提供了一个多功能平台，用于询问神经电路级原则和 我提出了虚拟啮齿动物的机制。这只silico动物将有一个 像真实老鼠一样的身体，体验正常的物理学，并接受训练以产生自然主义的大鼠行为。它将有 可以完全审问，操纵和重新配置的人造大脑。建立这个平台后，我 将开发一种分析方法，比较从自由移动动物到网络的体内神经活动 模型的表示。这项努力扩大了与神经表示联系的最新方法 在感觉系统中，任务优化的人工模型的表示，可以比较神经 在运动域和复杂行为中具有分析模型的活性。然后我建议进一步 开发虚拟啮齿动物来探究与动物的分层控制和运动学习有关的问题， 机器。 在这项拟议的研究的F99阶段，我将继续开发虚拟啮齿动物作为研究的平台 自然行为的人工和生物控制。具体来说，在AIM 1中，我将最终确定行为 测量，处理和建模管道以训练人工神经网络，以模仿 在物理模拟器中，实际的啮齿动物，验证其性能，并证明其实用性作为模型 具体的电机控制。在AIM 2中，我将在自由移动时从真实啮齿动物的汽车中心记录下来 将其神经活动与制定相同多样化运动的模型的网络活动进行比较。 在这项拟议的研究的K00阶段，我将扩展虚拟啮齿动物模型以研究层次结构 控制，柔性和自适应哺乳动物控制的保守特征。我将训练人工神经网络 作为F99阶段的一部分创建的重用较低级别的控制模块，以自主解决电机任务 用于运动神经科学研究。这个目标对运动神经科学领域具有很大的价值 促进对执行受控任务的动物的神经活动与网络活动的比较 执行相同任务的物理模拟类似物的分析模型。这些目的在一起提供了 运动神经控制的新道路，它包含行为的复杂性和 生物力学能够促进我们对健康和疾病中灵活和适应性运动控制的理解。