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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. 

The B4/B5 neurons in Aplysia californica have multiple functions. In this study, we further characterized B4/B5’s sensory responses to mechanical stimuli at different locations in the feeding apparatus and identified their direct motor effect on the muscles. The bidirectional signaling of B4/B5, which was also observed in freely feeding animals, suggests that these neurons may play a role in detecting nociceptive or proprioceptive information and regulating the movements that facilitate particular feeding behaviors. 

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. 

Abstract Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control inAplysia californica. Using the Synthetic Nervous System framework, we developed a model ofAplysiafeeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding. 

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.