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This review explores biorealistic neuromechanical models that integrate artificial neural controllers with biomechanical simulations to decode the complex interplay between nervous systems and behavior. By enabling inference of unmeasurable biophysical variables and generating testable hypotheses, these computational frameworks bridge critical gaps between neuroscience, robotics, and machine learning for applications in healthcare. The work underscores a shift toward embodied AI where algorithmic control is strictly coupled with physical body dynamics rather than existing as isolated software agents.
The embodied brain: Bridging the brain, body, and behavior with biorealistic neuromechanical models
arXiv:2601.08056v4 Announce Type: replace-cross 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. Computational models that embed artificial neural controllers within body models in simulated environments are a powerful tool for this purpose. Here, we review advances in biorealistic neuromechanical models 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 perturbations, one can generate new experimentally testable hypotheses using these models. We then examine how neuromechanical models facilitate the exchange among neuroscience, robotics, and machine learning, and showcase their applications in healthcare. We envision that coupling experimental studies with active probing of their neuromechanical surrogates will significantly accelerate progress in neuroscience.
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. Computational models that embed artificial neural controllers within body models in simulated environments are a powerful tool for this purpose. Here, we review advances in biorealistic neuromechanical models 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 perturbations, one can generate new experimentally testable hypotheses using these models. We then examine how neuromechanical models facilitate the exchange among neuroscience, robotics, and machine learning, and showcase their applications in healthcare. We envision that coupling experimental studies with active probing of their neuromechanical surrogates will significantly accelerate progress in neuroscience.
Neurons and Cognition (q-bio.NC); Robotics (cs.RO)
arXiv:2601.08056 [q-bio.NC]
(or arXiv:2601.08056v4 [q-bio.NC] for this version)
https://doi.org/10.1016/j.conb.2026.103244 DOI(s) linking to related resources
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