The embodied brain: Bridging the brain, body, and behavior with neuromechanical digital twins
For neuroscientists and roboticists, this review synthesizes current advances and future opportunities in using digital twins to study brain-body-environment interactions, but it is a review without novel experimental results.
This review discusses how neuromechanical digital twins—computational models combining artificial neural controllers with realistic body models in simulated environments—can infer biophysical variables, generate testable hypotheses, and facilitate exchange between neuroscience, robotics, and machine learning, with applications in healthcare.
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.