2020
DOI: 10.1007/s00422-020-00851-9
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Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

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 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 … Show more

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Cited by 21 publications
(31 citation statements)
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“…In light of this, an efficient design is for neural networks to be capable of flexibly switching between various functions upon demand [1,2,3,4,5,6,7,8,9,10,11], instead of having each task be performed by a different network [1,4,12,13,14]. This flexibility has indeed been observed experimentally [3,4,15,16,17,18,19,20], but how this flexibility came to be remains a topic of investigation.…”
Section: Introductionmentioning
confidence: 99%
“…In light of this, an efficient design is for neural networks to be capable of flexibly switching between various functions upon demand [1,2,3,4,5,6,7,8,9,10,11], instead of having each task be performed by a different network [1,4,12,13,14]. This flexibility has indeed been observed experimentally [3,4,15,16,17,18,19,20], but how this flexibility came to be remains a topic of investigation.…”
Section: Introductionmentioning
confidence: 99%
“…Recent progress in neuromorphic sensory systems which mimic the biological receptor functions and sensorial processing [129][130][131][132] trends toward sensors and sensory systems that communicate through asynchronous digital signals analogous to neural spikes [127], improving the performance of sensors and suggesting novel sensory pro- [116,119,120], for multifunctional robotic movement command/control based on the functional neuroanatomy and synaptic plasticity of Aplysia motor and interneurons (see Figure 3).…”
Section: Sensorimotor Integrationmentioning
confidence: 99%
“…These simulate behavioral switching in response to external sensory cues. Model approaches incorporate synaptic learning and neural connectivity in a simple mechanical model of the feeding apparatus [116], with testable hypotheses in the context of robot movement control. As explained in detail in Section 3.1, the neural networks that govern feeding in Aplysia include motor neurons and cerebral-buccal target interneurons.…”
Section: Repetitive or Rhythmic Behaviormentioning
confidence: 99%
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“…[26] constructed a model capable of transitioning between limitcycling and heteroclinic-cycling behaviors to represent a neuromotor central pattern generator (CPG) in Aplysia californica (see also [29]). While more detailed models for the Aplysia feeding system have since been developed [48], the simplicity of the three-component SLG (Shaw-Lyttle-Gill) model makes it an attractive target for analysis.…”
mentioning
confidence: 99%