Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
Biol Cybern. 2020 Dec;114(6):557-588. doi: 10.1007/s00422-020-00851-9. Epub 2020 Dec 10.
Animals exhibit remarkable feats of behavioral flexibility and multifunctional control that remain challenging for robotic systems. The neural and morphological basis of multifunctionality in animals can provide a source of bioinspiration for robotic controllers. However, many existing approaches to modeling biological neural networks rely on computationally expensive models and tend to focus solely on the nervous system, often neglecting the biomechanics of the periphery. As a consequence, while these models are excellent tools for neuroscience, they fail to predict functional behavior in real time, which is a critical capability for robotic control. To meet the need for real-time multifunctional control, we have developed a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time. Using this approach, we present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors (biting, swallowing, and rejection), demonstrates behavioral switching in response to external sensory cues, and incorporates both known neural connectivity and a simple bioinspired mechanical model of the feeding apparatus. We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
动物表现出非凡的行为灵活性和多功能控制能力，这对机器人系统来说仍然具有挑战性。动物多功能性的神经和形态基础可以为机器人控制器提供生物灵感的来源。然而，许多现有的生物神经网络建模方法依赖于计算成本高昂的模型，并且往往只关注神经系统，经常忽略外围的生物力学。因此，虽然这些模型是神经科学的优秀工具，但它们无法实时预测功能行为，这是机器人控制的关键能力。为了满足实时多功能控制的需求，我们开发了一种混合布尔模型框架，能够以快于实时的速度模拟神经爆发活动和简单的生物力学。使用这种方法，我们提出了一种加利福尼亚海兔摄食的多功能模型，该模型定性地再现了三种关键的摄食行为（咬、吞咽和拒绝），能够响应外部感觉线索进行行为切换，并结合了已知的神经连接和摄食器官的简单仿生机械模型。我们证明该模型可用于制定可测试的假设，并讨论了这种方法对机器人控制和神经科学的意义。
