The virtual rodent: a platform to study the artificial and biological control of natural behavior

项目概要 在不确定的环境中控制复杂的身体是一项挑战，我们的大脑已经进化到完美，但 实现灵活和鲁棒控制的算法和神经网络实现一直很难 识别。该提案的前提是，通过接受问题的复杂性来推动进步。 潜在的控制系统，包括它们控制的身体和动物行为的多样性。为了测试这个 想法，更一般地说，提供一个通用平台来询问神经电路级原理和 为了理解具体运动控制的潜在机制，我提出了虚拟啮齿动物。这种计算机动物将有一个 像真正的老鼠一样身体，体验正常的物理现象，并接受训练以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造大脑。建立这个平台后，我 将开发一种分析方法来比较自由移动的动物与网络的体内神经活动 模型的表示。这项工作扩展了最近连接神经表征的方法 与感觉系统中任务优化的人工模型的表示，能够比较神经 运动领域和复杂行为期间的分析模型的活动。然后我建议进一步 开发虚拟啮齿动物来探究与动物的层次控制和运动学习相关的问题 机器。 在本研究的 F99 阶段，我将继续开发虚拟啮齿动物作为研究平台 对自然行为的人工和生物控制。具体来说，在目标 1 中，我将最终确定行为 测量、处理和建模管道，用于训练人工神经网络来模仿 在物理模拟器中使用真实的啮齿动物，验证其性能，并展示其作为模型的实用性 体现了运动控制。在目标 2 中，我将记录真实啮齿动物自由移动和移动的运动中枢。 将它们的神经活动与执行相同不同动作的模型的网络活动进行比较。 在这项拟议研究的 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。我将训练一个人工神经网络 重用作为 F99 阶段一部分创建的较低级控制模块来自动解决常见的电机任务 用于运动神经科学研究。这一目标对于运动神经科学领域具有重要价值，因为它将 促进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模拟的分析模型。这些目标共同提供了 运动神经控制研究的新路径，涵盖行为的复杂性和 生物力学促进我们对健康和疾病中灵活和适应性运动控制的理解。

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.