Control for Multifunctionality: Bioinspired Control Based on Feeding in
Aplysia californica
- URL: http://arxiv.org/abs/2008.04978v2
- Date: Sat, 21 Nov 2020 14:25:23 GMT
- Title: Control for Multifunctionality: Bioinspired Control Based on Feeding in
Aplysia californica
- Authors: Victoria A. Webster-Wood, Jeffrey P. Gill, Peter J. Thomas, Hillel J.
Chiel
- Abstract summary: We develop a hybrid Boolean model framework capable of modeling neural bursting activity and simple biomechanics at speeds faster than real time.
We present a multifunctional model of Aplysia californica feeding that qualitatively reproduces three key feeding behaviors.
We demonstrate that the model can be used for formulating testable hypotheses and discuss the implications of this approach for robotic control and neuroscience.
- Score: 0.3277163122167433
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 bio-inspiration 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.
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