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A virtual rodent predicts the structure of neural activity across behaviors

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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.

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Correspondence to Diego Aldarondo or Bence P. Ölveczky.

Supplementary information

Supplementary Information

This file contains Supplementary Discussion and Supplementary Tables 1-3.

Reporting Summary

Supplementary Video 1

Overview of the MIMIC pipeline. The MIMIC pipeline consists of multi-camera video acquisition.

Supplementary Video 2

Accurate 3D pose estimation with DANNCE. We used DANNCE to estimate the 3D pose of freely moving rats from multi-camera recordings. This video depicts the DANNCE keypoint estimates overlain atop the original video recordings from all six cameras. Keypoint estimates are accurate across a wide range of behaviors.

Supplementary Video 3

Accurate skeletal registration with STAC. We used a custom implementation of simultaneous tracking and calibration (STAC).

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Aldarondo, D., Merel, J., Marshall, J.D. et al. A virtual rodent predicts the structure of neural activity across behaviors. Nature (2024).

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