美国哈佛大学Bence P. Ölveczky等研究人员合作发现，虚拟啮齿动物可预测各种行为的神经活动结构。2024年6月11日，《自然》杂志在线发表了这项成果。

研究人员表示，动物对自己的身体有着精湛的控制能力，因此能够做出各种行为。然而，大脑是如何实现这种控制的仍不清楚。要加深人们的理解，就需要建立模型，将控制原理与行为动物的神经活动结构联系起来。

为此，研究人员建立了一个“虚拟啮齿动物”，由人工神经网络在物理模拟器中驱动生物力学逼真的大鼠模型。研究人员使用深度强化学习来训练了虚拟代理模仿自由移动的大鼠行为，从而使其能够将记录在真实大鼠身上的神经活动与模仿其行为的虚拟啮齿动物的网络活动进行比较。研究人员发现，虚拟啮齿动物的网络活动比真实大鼠的任何运动特征，都能更好地预测感觉运动纹状体和运动皮层的神经活动，这与这两个区域都执行反动力学是一致的。

此外，网络的潜在可变性预测了神经在不同行为中的可变性结构，并以符合最佳反馈控制的最小干预原则的方式提供了稳健性。这些结果表明，对生物力学逼真的虚拟动物进行物理模拟，有助于解释不同行为的神经活动结构，并将其与运动控制的理论原则联系起来。

附：英文原文

Title: A virtual rodent predicts the structure of neural activity across behaviors

Author: Aldarondo, Diego, Merel, Josh, Marshall, Jesse D., Hasenclever, Leonard, Klibaite, Ugne, Gellis, Amanda, Tassa, Yuval, Wayne, Greg, Botvinick, Matthew, lveczky, Bence P.

Issue&Volume: 2024-06-11

Abstract: Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviors. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. To facilitate this, we built a ‘virtual rodent’, in which an artificial neural network actuates a biomechanically realistic model of the rat 1 in a physics simulator 2. We used deep reinforcement learning 3–5 to train the virtual agent to imitate the behavior of freely-moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behavior. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent’s network activity than by any features of the real rat’s movements, consistent with both regions implementing inverse dynamics 6. Furthermore, the network’s latent variability predicted the structure of neural variability across behaviors and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control 7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behavior and relate it to theoretical principles of motor control.

DOI: 10.1038/s41586-024-07633-4

Source: https://www.nature.com/articles/s41586-024-07633-4