Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model

Abstract

Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording. Neuromechanical simulations can provide insights by modeling the locomotor circuits. Past neuromechanical models proposed control of locomotion either driven by central pattern generators (CPGs) with simple sensory commands or by a purely reflex-based network regulated by state-machine mechanisms. However, the physiological interpretation of these state-machines remains unclear. Here, we present a physiologically plausible model to investigate spinal control and modulation of human locomotion. We propose a bio-inspired controller composed of two coupled central pattern generators (CPGs) that produce the rhythm and pattern and a reflex-based network simulating low-level reflex pathways and Renshaw cells. This reflex network is based on leaky-integration neurons, and the whole system does not rely on changing reflex gains according to the gait cycle state. The only component of the controller that maintains a state-machine mechanism is the balance controller of the trunk. The musculoskeletal model is composed of a skeletal structure and 9 muscles per leg generating movement in sagittal plane. After optimizing the open parameters, human locomotion replicating kinematics and muscle activation naturally emerged. Furthermore, when CPGs were not activated, no stable and physiologically plausible gaits could be achieved through optimization, suggesting the necessity of this component to generate rhythmic behavior without a state machine mechanism regulating reflex activation. The controller could reproduce a wide range of speeds from 0.3 to 1.9 m/s. The results also showed that the net influence of feedback on motoneurons during perturbed locomotion is predominantly inhibitory and that the CPG network provides the timing of motoneurons' activation by exciting or inhibiting muscles in specific gait phases. The proposed bio-inspired controller could contribute to our understanding of locomotor circuits of the intact spinal cord and could be used to study neuromotor disorders.

New neural-inspired controller generalises modularity over lower limb tasks through internal models

“…Thus, if the reflexes and the anatomy of the distal leg are improperly integrated with the rest of the system, the dynamics at this joint may be affected. For example, Dzeladini et al [2014], Di Russo et al [2023], and Ramadan et al [2021] presented models with alterations at the ankles, the two last having discrepancies resembling those in the MPL model. Schumacher et al [2023] has shown results matching consistently with the kinematic data, but also presented abnormal trajectories for the ankle that were depending on the selection of the musculoskeletal model.…”

“…Thus, if the reflexes and the anatomy of the distal leg are improperly integrated with the rest of the system, the dynamics at this joint may be affected. For example, Song and Geyer [2015], Di Russo et al [2023], and Ramadan et al [2021] presented models with alterations at the ankles, the two last having discrepancies resembling those in the MPL model. Geyer and Herr [2010] presents ankle kinematics that fit better experimental data than our model, and Dzeladini et al [2014] have also a better trajectory, especially if the controller is reflex-dependent.…”

“…Although a natural next step for this work is to apply the current MPL controller to a more complex musculoskeletal model, there is also scope for further development of the controller. Here, as in similar works [Di Russo et al, 2023], we use sinusoidal bursts for the synergies, although more physiological activation profiles could be devised.…”

“…Predictive Neuromuscular Modelling (PNM) can address the gaps in the knowledge of human locomotion by testing hypotheses about the inherent neurophysiology of gait [De Groote and Falisse, 2021]. PNM can be used to devise the commands relative to a task, characterized by an objective function, at the level of the actuators [De Groote et al, 2016, Falisse et al, 2019 or of a hypothetical control architecture [Di Russo et al, 2023, Taga et al, 1991, Geyer and Herr, 2010, Muñoz et al, 2022. In the latter case, the hypothetical motor control is implemented as a control policy, consisting of a set of equations representing ruling principles of motion.…”

“…However, evidence suggests that CPG, if they exist in humans, would be dependent on an intact sensory system to generate locomotion [Capaday, 2002]. Hybrid models encompassing both reflexes, CPGs, or other components of feedforward control [Di Russo et al, 2023, Ramadan et al, 2021 show versatility and physiological plausibility, and appear to better capture the complexity of human locomotor control. However, controller-based PNM to this day has presented only control architectures that are specific to a task (usually steady state gait) and do not present the flexibility for representing drastically different gait and balance behaviors.…”