An official website of the United States government
Living systems can use a single periphery to perform a variety of tasks and adapt to a dynamic environment. This multifunctionality is achieved through the use of neural circuitry that adaptively controls the reconfigurable musculature. Current robotic systems struggle to flexibly adapt to unstructured environments. Through mimicry of the neuromechanical coupling seen in living organisms, robotic systems could potentially achieve greater autonomy. The tractable neuromechanics of the sea slug Aplysia californica’s feeding apparatus, or buccal mass, make it an ideal candidate for applying neuromechanical principles to the control of a soft robot. In this work, a robotic grasper was designed to mimic specific morphology of the Aplysia feeding apparatus. These include the use of soft actuators akin to biological muscle, a deformable grasping surface, and a similar muscular architecture. A previously developed Boolean neural controller was then adapted for the control of this soft robotic system. The robot was capable of qualitatively replicating swallowing behavior by cyclically ingesting a plastic tube. The robot’s normalized translational and rotational kinematics of the odontophore followed profiles observed in vivo despite morphological differences. This brings Aplysia-inspired control in roboto one step closer to multifunctional neural control schema in vivo and in silico. Future additions may improve SLUGBOT’s viability as a neuromechanical research platform.
more »
« less
Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica
Abstract The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system ofAplysia californicais an excellent model for investigating questions in soft body systems’ neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for movingAplysia’s grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions ofAplysia’s grasper, has not been characterized. Because of the importance of the musculature’s properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior ofAplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle’s peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex’s kinematics in vivo.
more »
« less
- Award ID(s):
- 2015317
- PAR ID:
- 10627475
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Biological Cybernetics
- Volume:
- 118
- Issue:
- 3-4
- ISSN:
- 1432-0770
- Page Range / eLocation ID:
- 165 to 185
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine molluskAplysia californica. Previousin silicoandin robotomodels have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to matchin vivodata both qualitatively and quantitatively. We introduce a new neuromechanical model ofAplysiafeeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics ofAplysiafeeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulated feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen inAplysiaand demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass.more » « less
-
Background/Objectives: This study explores an optimization-based strategy for muscle force estimation by employing simplified cost functions integrated with physiologically relevant muscle models. Methods: Considering elbow flexion as a case study, we employ an inverse-dynamics approach to estimate muscle forces for the biceps brachii, brachialis, and brachioradialis, utilizing different combinations of cost functions and muscle constitutive models. Muscle force generation is modeled by accounting for active and passive contractile behavior to varying degrees using Hill-type models. In total, three separate cost functions (minimization of total muscle force, mechanical work, and muscle stress) are evaluated with each muscle force model to represent potential neuromuscular control strategies without relying on electromyography (EMG) data, thereby characterizing the interplay between muscle models and cost functions. Results: Among the evaluated models, the Hill-type muscle model that incorporates both active and passive properties, combined with the stress minimization cost function, provided the most accurate predictions of muscle activation and force production for all three arm flexor muscles. Our results, validated against existing biomechanical data, demonstrate that even simplified cost functions, when paired with detailed muscle models, can achieve high accuracy in predicting muscle forces. Conclusions: This approach offers a versatile, EMG-free alternative for estimating muscle recruitment and force production, providing a more accessible and adaptable tool for muscle force analysis. It has profound implications for enhancing rehabilitation protocols and athletic training, not only broadening the applicability of muscle force estimation in clinical and sports settings but also paving the way for future innovations in biomechanical research.more » « less
-
The mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviors, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e., always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should both scale identically (proportional to mass⅔), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioral duration. We tested this hypothesis by measuring the duration of feeding behaviors in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as Aplysia attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviors scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behavior, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro computed tomography scans (microCT) of buccal masses. Consequently, larger Aplysia did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioral durations in quasistatic systems.more » « less
-
ABSTRACT Hill-type muscle models are widely used, even though they do not accurately represent the relationship between activation and force in dynamic contractions. We explored the use of neural networks as an alternative approach to capture features of dynamic muscle function, without a priori assumptions about force–length–velocity relationships. We trained neural networks using an existing dataset of two guinea fowl muscles to estimate muscle force from activation, fascicle length and velocity. Training data were recorded using sonomicrometry, electromyography and a tendon buckle. First, we compared the neural networks with Hill-type muscle models, using the same data for network training and model optimization. Second, we trained neural networks on larger datasets, in a more realistic machine learning scenario. We found that neural networks generally yielded higher coefficients of determination and lower errors than Hill-type muscle models. Neural networks performed better when estimating forces on the muscle used for training, but on another bird, than on a different muscle of the same bird, likely due to inaccuracies in activation and force scaling. We extracted force–length and force–velocity relationships from the trained neural networks and found that both effects were underestimated and the relationships were not well replicated outside the training data distribution. We discuss suggested experimental designs and the challenge of collecting suitable training data. Given a suitable training dataset, neural networks could provide a useful alternative to Hill-type muscle models, particularly for modeling muscle dynamics in faster movements; however, scaling of the training data should be comparable between muscles and animals.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00422-024-00990-3
