Topic: limb kinematics

Computational models have been seminal in expanding our understanding of how neural, biomechanical, or physical elements can give rise to behavior. With the forthcoming emergence of nearly complete connectomes of the central nervous system, musculature, and biomechanics of Drosophila, it is only now possible to construct models grounded in anatomy and physiology. Biological experiments, in reciprocity, could complement these models, verifying and enhancing their accuracy. Here, we leverage a biomechanical model of Drosophila to build, for the first time, a Hill-type muscle model using X-ray sc

The cerebellum plays a key role in motor learning, adapting movements in response to perturbations or errors. For simple tasks like eyeblink classical conditioning, climbing fibers originating in the inferior olive and projecting to the cerebellum provide neural instructive signals sufficient to drive learning. For complex whole-body behaviors like locomotion, however, it is not known what role climbing fibers play. We recently demonstrated (Darmohray et al., 2019) that mice running on a split-belt treadmill (that controls the speed under each side of the body independently) exhibit a form of

Integrating sensory information, such as visual inputs, is critical for many goal-directed behaviors, including motor learning. Motor adaptation, a specific type of adaptive learning where one needs to rapidly learn a new sensorimotor mapping has been an excellent way to behaviorally test how motor learning may be enacted. For example, in humans, perturbing visual feedback results in deviated trajectories that participants can restore to baseline performance over time. Yet, the neural circuits that underlie these visuomotor adaptations remain poorly understood. Here, we developed an implicit v