Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th Annual Neuro-inspired Computational Elements Workshop |
| Number of pages | 8 |
| Publication status | Accepted/In press - 2020 |
| Event | 2020 Neuro-Inspired Computational Elements Workshop - Heidelberg University, Heidelberg, Germany Duration: 17 Mar 2020 → 20 Mar 2020 |
Conference
| Conference | 2020 Neuro-Inspired Computational Elements Workshop |
|---|---|
| Location | Heidelberg University |
| Country | Germany |
| City | Heidelberg |
| Period | 17/03/2020 → 20/03/2020 |
Keywords
- Bio control
- Learning algorithms
- Adaptive locomotion
- Brain models
- Motor control
Cite this
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Adaptive control for hindlimb locomotion in a simulated mouse through temporal cerebellar learning. / Jensen, Thomas Passer; Tata, S.; Ijspeert, A. J.; Tolu, Silvia.
Proceedings of the 8th Annual Neuro-inspired Computational Elements Workshop. 2020.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - Adaptive control for hindlimb locomotion in a simulated mouse through temporal cerebellar learning
AU - Jensen, Thomas Passer
AU - Tata, S.
AU - Ijspeert, A. J.
AU - Tolu, Silvia
PY - 2020
Y1 - 2020
N2 - Human beings and other vertebrates show remarkable performance and efficiency in locomotion, but the functioning of their biological control systems for locomotion is still only partially understood. The basic patterns and timing for locomotion are provided by a central pattern generator (CPG) in the spinal cord. The cerebellum is known to play an important role in adaptive locomotion. Recent studies have given insights into the error signals responsible for driving the cerebellar adaptation in locomotion. However, the question of how the cerebellar output influences the gait remains unanswered. We hypothesize that the cerebellar correction is applied to the pattern formation part of the CPG. Here, a bio-inspired control system for adaptive locomotion of the musculoskeletal system of the mouse is presented, where a cerebellar-like module adapts the step time by using the double support interlimb asymmetry as a temporal teaching signal. The control system is tested on a simulated mouse in a split-belt treadmill setup similar to those used in experiments with real mice. The results show adaptive locomotion behavior in the interlimb parameters similar to that seen in humans and mice. The control system adaptively decreases the double support asymmetry that occurs due to environmental perturbations in the split-belt protocol.
AB - Human beings and other vertebrates show remarkable performance and efficiency in locomotion, but the functioning of their biological control systems for locomotion is still only partially understood. The basic patterns and timing for locomotion are provided by a central pattern generator (CPG) in the spinal cord. The cerebellum is known to play an important role in adaptive locomotion. Recent studies have given insights into the error signals responsible for driving the cerebellar adaptation in locomotion. However, the question of how the cerebellar output influences the gait remains unanswered. We hypothesize that the cerebellar correction is applied to the pattern formation part of the CPG. Here, a bio-inspired control system for adaptive locomotion of the musculoskeletal system of the mouse is presented, where a cerebellar-like module adapts the step time by using the double support interlimb asymmetry as a temporal teaching signal. The control system is tested on a simulated mouse in a split-belt treadmill setup similar to those used in experiments with real mice. The results show adaptive locomotion behavior in the interlimb parameters similar to that seen in humans and mice. The control system adaptively decreases the double support asymmetry that occurs due to environmental perturbations in the split-belt protocol.
KW - Bio control
KW - Learning algorithms
KW - Adaptive locomotion
KW - Brain models
KW - Motor control
M3 - Article in proceedings
BT - Proceedings of the 8th Annual Neuro-inspired Computational Elements Workshop
ER -