A neural circuitry that emphasizes spinal feedbacks generates diverse behaviors of human locomotion - Robotics Institute Carnegie Mellon University

A neural circuitry that emphasizes spinal feedbacks generates diverse behaviors of human locomotion

Journal Article, Journal of Physiology, Vol. 593, No. 16, pp. 3493 - 3511, April, 2015

Abstract

Neural networks along the spinal cord contribute substantially to generating locomotion behaviours in humans and other legged animals. However, the neural circuitry involved in this spinal control remains unclear. We here propose a specific circuitry that emphasizes feedback integration over central pattern generation. The circuitry is based on neurophysiologically plausible muscle-reflex pathways that are organized in ten spinal modules realizing limb functions essential to legged systems in stance and swing. These modules are combined with a supraspinal control layer that adjusts the
desired foot placements and selects the leg that is to transition into swing control during double support. Using physics-based simulation, we test the proposed circuitry in a neuromuscular human model that includes neural transmission delays, musculotendon dynamics, and compliant foot-ground contacts. We find that the control network is sufficient to compose steady and transitional 3D locomotion behaviours including walking and running, acceleration and deceleration, slope and stair negotiation, turning, and deliberate obstacle avoidance. The results suggest feedback integration to be functionally more important than central pattern generation in human locomotion across behaviours. In addition, the proposed control architecture may serve as a guide in the search for the neurophysiological origin and circuitry of spinal control in human.

Notes
Accepted Article, 2015

BibTeX

@article{Song-2015-102645,
author = {Seungmoon Song and Hartmut Geyer},
title = {A neural circuitry that emphasizes spinal feedbacks generates diverse behaviors of human locomotion},
journal = {Journal of Physiology},
year = {2015},
month = {April},
volume = {593},
number = {16},
pages = {3493 - 3511},
}