期刊
BIOMIMETIC AND BIOHYBRID SYSTEMS, LIVING MACHINES 2022
卷 13548, 期 -, 页码 354-365出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-20470-8_35
关键词
Multifunctionality; Computational neuroscience; Aplysia; Control; Synthetic nervous systems
资金
- NSF as part of the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program [DBI 2015317]
- NSF [IIS1704436]
By building a synthetic neuromechanical model, we are able to reproduce the feeding behaviors of Aplysia and the model shows good agreement with experimental data. The model also has predictive ability and can be used for generating and testing hypotheses about Aplysia feeding control.
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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