Abstract
This work demonstrates a neuromechanical model of rat hindlimb locomotion undergoing nominal walking with perturbations. In the animal, two types of responses to perturbations are observed: resetting and non-resetting deletions. This suggests that the animal locomotor system contains a memory-like organization. To model this phenomenon, we built a synthetic nervous system that uses separate rhythm generator and pattern formation layers to activate antagonistic muscle pairs about each joint in the sagittal plane. Our model replicates the resetting and non-resetting deletions observed in the animal. In addition, in the intact (i.e., fully afferented) rat walking simulation, we observe slower recovery after perturbation, which is different from the deafferented animal experiment. These results demonstrate that our model is a biologically feasible description of some of the neural circuits in the mammalian spinal cord that control locomotion, and the difference between our simulation and fictive motion shows the importance of sensory feedback on motor output. This model also demonstrates how the pattern formation network can activate muscle synergies in a coordinated way to produce stable walking, which motivates the use of more complex synergies activating more muscles in the legs for three-dimensional limb motion.
Highlights
Neural circuits in the spinal cord, called central pattern generators (CPGs), generate neural oscillations and control rhythmic movements
The top layer is the rhythm generator layer (Figure 4B), which is located in the spinal cord in vertebrates
We modeled our rhythm generator layer (Figure 4B), which is located in the spinal cord in vertebrates
Summary
Neural circuits in the spinal cord, called central pattern generators (CPGs), generate neural oscillations and control rhythmic movements. These CPGs are responsible for repetitive motions such as walking [1,2], swimming [3], and breathing [4]. It is still unclear how CPGs contribute to motoneuron activations and muscle forces. Understanding this relationship will contribute to many fields, including understanding the effects of disease on locomotor circuits, repair and rehabilitation of injuries [5], and even controlling robots. Little is known about the details of motoneuron activity when animals perform steady walking, as it is difficult to record from individual neurons
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