Building an accurate computational model can clarify the basis of feeding behaviors in Aplysia californica. We introduce a specific circuitry model that emphasizes feedback integration. The circuitry uses a Synthetic Nervous System, a biologically plausible neural model, with motor neurons and buccal ganglion interneurons organized into 9 subnetworks realizing functions essential to feeding control during the protraction and retraction phases of feeding. These subnetworks are combined with a cerebral ganglion layer that controls transitions between feeding behaviors. This Synthetic Nervous System is connected to a simplified biomechanical model of Aplysia and afferent pathways provide proprioceptive and exteroceptive feedback to the controller. The feedback allows the model to coordinate and control its behaviors in response to the external environment. We find that the model can qualitatively reproduce multifunctional feeding behaviors. The kinematic and dynamic responses of the model also share similar features with experimental data. The results suggest that this neuromechanical model has predictive ability and could be used for generating or testing hypotheses about Aplysia feeding control.
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SLUGBOT, an Aplysia-Inspired Robotic Grasper for Studying Control
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.
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- Award ID(s):
- 2015317
- PAR ID:
- 10424817
- Date Published:
- Journal Name:
- Biomimetic and Biohybrid Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-031-20470-8_19
