Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
Biol Cybern. 2024 Aug;118(3-4):187-213. doi: 10.1007/s00422-024-00991-2. Epub 2024 May 20.
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 Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding 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.
研究动物运动控制的神经系统可以揭示动物如何灵活适应不断变化的环境。我们专注于加利福尼亚海兔摄食控制的神经基础。使用合成神经系统框架，我们开发了一个加利福尼亚海兔摄食神经回路的模型，该模型平衡了神经生理学的合理性和计算的复杂性。该回路包括在现有文献中确定的神经元、突触和反馈途径。我们根据其功能作用将神经元组织成三个层次和五个子网。模拟结果表明，该回路模型可以捕获神经元和网络水平的固有动力学。当与简化的外围生物力学模型结合使用时，它足以介导三种类似动物的摄食行为（咬、吞和拒绝）。该模型的运动学、动力学和神经反应也与动物数据具有相似的特征。这些结果强调了感觉反馈在摄食过程中的功能作用。
