The embodied brain: Bridging the brain, body, and behavior with neuromechanical digital twins

TL;DR

Neuromechanical digital twins integrate neural, mechanical, and environmental models to advance understanding of animal behavior, facilitate hypothesis generation, and foster interdisciplinary applications in neuroscience and robotics.

Contribution

This review highlights recent advances in neuromechanical digital twins and explores their potential to accelerate neuroscience research and interdisciplinary collaboration.

Findings

Models enable inference of biophysical variables difficult to measure experimentally.

Systematic perturbation of models generates testable hypotheses.

Applications span neuroscience, robotics, machine learning, and healthcare.

Abstract

Animal behavior reflects interactions between the nervous system, body, and environment. Therefore, biomechanics and environmental context must be considered to understand algorithms for behavioral control. Neuromechanical digital twins, namely computational models that embed artificial neural controllers within realistic body models in simulated environments, are a powerful tool for this purpose. Here, we review advances in neuromechanical digital twins while also highlighting emerging opportunities ahead. We first show how these models enable inference of biophysical variables that are difficult to measure experimentally. Through systematic perturbation, one can generate new experimentally testable hypotheses through these models. We then examine how neuromechanical twins facilitate the exchange between neuroscience, robotics, and machine learning, and showcase their applications in healthcare. We envision that coupling experimental studies with active probing of their neuromechanical twins will significantly accelerate progress in neuroscience.