Decoding the Neural Mechanisms Underlying Locomotion Using Mathematical Models and Bio-inspired Robots: From Lamprey to Human Locomotion
The ability to efficiently move in complex environments is a fundamental property both for animals and for robots, and the problem of locomotion and movement control is an area in which neuroscience and robotics can fruitfully interact. Animal locomotion control is in a large part based on spinal cord circuits that combine reflex loops and central pattern generators (CPGs), i.e. neural networks capable of producing complex rhythmic or discrete patterns while being activated and modulated by relatively simple control signals. These networks located in the spinal cord for vertebrate animals are modulated by descending control signals and interact with the musculoskeletal system for generating rich motor behaviors. This paper presents how numerical models and robots can be used to explore the interplay of these four components (CPGs, reflexes, descending modulation, and musculoskeletal system). Going from lamprey to human locomotion, a series of models are presented that tend to show that the respective roles of these components have changed during evolution with a dominant role of CPGs in lamprey and salamander locomotion, and a more important role for sensory feedback and descending modulation in human locomotion. Interesting properties for robot locomotion control are also discussed.
Swiss National Science Foundation (project CR23I2_140714), the Swiss National Center of Competence in Research in Robotics, the European Commission (projects Walkman FP7-ICT 611832, Symbitron FP7-ICT 661626, and Cogimon H2020 ICT-23-2014 644727), and the Envirobot project funded by the Swiss NanoTera program.
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