Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

Springer Science and Business Media LLC - Tập 114 - Trang 557-588 - 2020
Victoria A. Webster-Wood1,2,3, Jeffrey P. Gill4, Peter J. Thomas5,6,7, Hillel J. Chiel4,8,9
1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, USA
2Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, USA
3McGowan Institute for Regenerative Medicine, Carnegie Mellon University, Pittsburgh, USA
4Department of Biology, Case Western Reserve University, Cleveland, USA
5Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, USA
6Department of Biology, Department of Cognitive Science, Case Western Reserve University, Cleveland, USA
7Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, USA
8Department of Neurosciences, Case Western Reserve University, Cleveland, USA
9Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA.

Tóm tắt

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.

Tài liệu tham khảo

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