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 在不确定的环境中控制复杂的物体是我们的大脑进化到完美的挑战，然而， 实现灵活和鲁棒控制的算法和神经网络实现已经很难 确认身份。这一建议是基于这样一种想法，即通过接受 底层控制系统，包括它们控制的身体和动物行为的多样性。为了验证这一 想法，更一般地说，提供了一个多功能的平台，用于询问神经回路级的原则， 机制下体现电机控制，我提出了虚拟啮齿动物。这种计算机模拟动物将具有 身体像一只真实的老鼠，体验正常的物理学，并被训练产生自然的老鼠行为。它将有 一个可以被完全询问、操纵和重新配置的人工大脑。建立这个平台后，我 将开发一种分析方法，将自由移动的动物的体内神经活动与网络进行比较 模型的表示。这一奋进扩展了最近的方法，将神经表征 利用感觉系统中任务优化的人工模型的表示，实现神经系统的比较 活动与分析模型在运动域和在复杂的行为。因此，我建议进一步 开发虚拟啮齿动物，以探索与动物的分层控制和运动学习相关的问题， 机械. 在本研究的F99阶段，我将继续以虚拟啮齿动物为平台进行研究 对自然行为的人工和生物控制。具体来说，在目标1中，我将完成一个行为 测量、处理和建模管道，以训练人工神经网络来模仿 真实的啮齿动物，而在物理模拟器，验证其性能，并证明其效用作为一个模型， 具体的电机控制。在目标2中，我将记录真实的啮齿动物的运动中心，因为它们自由移动， 将他们的神经活动与执行相同的不同动作的模型的网络活动进行比较。 在本研究的K00阶段，我将扩展虚拟啮齿动物模型来研究分层 控制，灵活和适应性哺乳动物控制的保守特征。我会训练一个人工神经网络 重复使用作为F99阶段一部分创建的较低级别的控制模块，以自主解决通常的运动任务 用于运动神经科学研究。这一目标对运动神经科学领域具有重要价值，因为它将 便于比较执行受控任务的动物的神经活动与 分析模型执行相同任务的物理模拟类似物。总之，这些目标提供了一个 这是研究运动神经控制的一条新途径，它包含了行为的复杂性， 生物力学，以促进我们对健康和疾病中灵活和自适应运动控制的理解。