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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.
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A Synthetic Nervous System Controls a Biomechanical Model of Aplysia Feeding
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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- Award ID(s):
- 2015317
- PAR ID:
- 10424814
- 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_35
