aiqbench analysis
This review examines biorealistic neuromechanical models that integrate artificial neural controllers with biomechanical simulations to decode animal behavior and generate testable hypotheses. By bridging neuroscience, robotics, and machine learning, these computational frameworks enable the inference of difficult-to-measure biophysical variables while offering new pathways for healthcare applications. The work highlights how coupling experimental data with active probing of digital surrogates can accelerate fundamental progress in understanding brain-body interactions.
The embodied brain: Bridging the brain, body, and behavior with biorealistic neuromechanical models
arXiv:2601.08056v3 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 perturbation, one can generate new experimentally testable hypotheses through these models. We then examine how neuromechanical models 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 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 perturbation, one can generate new experimentally testable hypotheses through these models. We then examine how neuromechanical models 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 surrogates will significantly accelerate progress in neuroscience.
Neurons and Cognition (q-bio.NC); Robotics (cs.RO)
arXiv:2601.08056 [q-bio.NC]
(or arXiv:2601.08056v3 [q-bio.NC] for this version)
Full text under the publisher's open licence · 143 words
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