P175 A Neurorobotic Framework for Exploring Locomotor Control Following Recovery from Thoracic Spinal Cord Injury
Andrew B. Lockhart*1, Huangrui Chu1, Shravan Tata Ramalingasetty1, Natalia A. Shevtsova1, David S.K. Magnuson2, Simon M. Danner1
1Department of Neurobiology and Anatomy, College of Medicine, Drexel University, Philadelphia, PA, USA
2Department of Neurological Surgery, University of Louisville, Louisville, KY, USA
*Email: abl73@drexel.edu
Introduction
Thoracic spinal cord contusion disrupts communication between the cervical and lumbar circuitry. Despite this, rats recover locomotor function, though at a reduced speed and with altered speed-dependent gait expression. Our previous computational model of spinal locomotor circuitry [2,3] reproduced the observed gait changes by linking them to impaired long propriospinal connectivity and lumbar circuitry reorganization, likely involving enhanced reliance on afferent feedback. To investigate the role of sensory feedback in locomotion and explore post-contusion reorganization, a neurorobotic model of quadrupedal locomotion was used in which the spinal circuitry was embedded in a body that interacted with the environment (Fig. 1).
Methods
We have expanded our previous neural network model of spinal locomotor circuitry to drive a simulated Unitree Go1 quadrupedal robot. The model includes four rhythm generators, one per limb, interconnected by commissural and long propriospinal neurons. Activity from each rhythm generator controls a pattern formation network that coordinates muscle activation in each limb. Hill-type muscles convert this activation into torque to actuate the motors and allow for calculation of proprioceptive feedback, which interacts with all levels of the spinal circuitry. Connection weights of proprioceptive, vestibular, and pattern forming neurons were optimized using covariance matrix adaptation evolution strategy to produce adaptive locomotion.
Results
The optimized model produces stable locomotion across a range of target speeds. Integration of muscle states and environmental information through proprioceptive, cutaneous, and vestibular neurons allows the model to traverse rough terrain consisting of variable slopes and ground friction. Preliminary simulation of thoracic contusion by reducing connection weights of inter-enlargement long propriospinal neurons results in altered gaits.
Discussion
The model provides a testbed for linking neuronal manipulations to changes in locomotion and behavior. By comparing locomotor gaits across models—those undergoing a second round of optimization post-contusion, those that have not, and experimental results from rats—we can identify and analyze critical neuronal connections involved in recovery. Using this approach, we will further investigate how circuit reorganization can contribute to locomotor recovery after thoracic spinal cord contusion.
Figure 1. Fig 1. A) The central locomotor circuit model for four limbs includes long propriospinal neurons connecting cervical and lumbar circuits adapted from Frigon 2017 [3]. B) Two-level rhythm and pattern formation circuitry for one limb. Motoneuron (MN) activity activates muscles (C) which actuate torque-controlled motors (D). Kinematics and kinetics are transformed into afferent feedback signals.
Acknowledgements
This work was supported by the National Institutes of Health (NIH) grantsR01NS112304, R01NS115900, andT32NS121768.
References
[1] Danner, S. M., et al. (2017). Computational modeling of spinal circuits controlling limb coordination and gaits in quadrupeds.eLife,6, e31050. https://doi.org/10.7554/eLife.31050
[2] Zhang, H., et al. (2022). The role of V3 neurons in speed-dependent interlimb coordination during locomotion in mice.eLife,11, e73424. https://doi.org/10.7554/eLife.73424
[3] Frigon, A. (2017). The neural control of interlimb coordination during mammalian locomotion.Journal of Neurophysiology,117(6), 2224–2241. https://doi.org/10.1152/jn.00978.2016
