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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This content will become publicly available on May 20, 2025
A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network
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 in
- Award ID(s):
- 2015317
- PAR ID:
- 10510858
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Biological Cybernetics
- ISSN:
- 1432-0770
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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