Application of neural oscillators to study the effects of walking speed on rhythmic activations at the ankle.

BACKGROUND: Spinal pattern generators (SPG) are neural networks in the spinal cord that do not require a central input from the brain to generate a motor output. We wanted to determine whether SPG can adapt to the changing motor demands from walking at different speeds, and performing silly walks. METHODS: An SPG model consisting of an oscillator made up of two neurons was utilised in this study; one neuron activates the soleus and the other activates the tibialis anterior. The outputs of the SPG model therefore represent the electromyographic measurements from each muscle. Seven healthy subjects were requested to perform silly walks, normal walking at self-selected speed (4.8 ± 0.5 km/h), 3.5 km/h, 4.0 km/h and 4.5 km/h on a treadmill. Loading and hip angles were used as inputs into the model. RESULTS: No significant differences in the model parameters were found between normal walking at self-selected speed and other walking speeds. Only the adaptation time constant for the ankle flexor during silly walks was significantly different from the other normal walking trials. CONCLUSION: We showed that SPG in the spinal cord can interpret and respond accordingly to velocity-dependent afferent information. Changes in walking speed do not require a different motor control mechanism provided there is no disruption to the alternating muscular activations generated at the ankle.

Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans.

Spinal pattern generators (SPGs), which are neural networks without a central input from the brain may be responsible for controlling locomotion. In this study, we used neural oscillators to examine the rhythmic patterns generated at the ankle during walking. Seven healthy male subjects were requested to walk at their normal self-selected speed on a treadmill. Force measurements acquired from pressure insoles, electromyography and kinematic data were captured simultaneously. The SPG model consisted of a simple oscillator made up of two neurons; one neuron will activate an ankle extensor and the other will activate an ankle flexor. The outputs of the oscillator represented the muscle activation of each muscle. A nonlinear least squares algorithm was used to determine a set of parameters that would optimise the differences between model output and experimental data. Insole forces and hip angles of six consecutive strides were used as inputs to the model, which generated outputs that closely fitted experimental data. Our results showed that it is possible to reproduce muscle activations using neural oscillators. A close correlation between simulated and measured muscle activations indicated that spinal control should not be underestimated in models of human locomotion.

Phasic bursting pattern of postural responses may reflect internal dynamics: simulation of trunk reflexes with a neural oscillator model.

Postural responses are usually investigated as reflexes. Several trials are averaged, and trial-to-trial variations are interpreted as noise. Several studies providing single-trial data plots revealed oscillations that may be cancelled out in averaged time series. Variations between single trials may also be interpreted as a consequence of changed dynamic properties of the neural circuitries. Therefore, we propose a Matsuoka oscillator model to describe single-trial postural responses to external perturbations. The applicability of the model was demonstrated by a comparison between simulations and experimental electromyographic (EMG) data. Vertical force perturbations of durations 0.4 s and 0.2 s were applied via a handle to 10 subjects. Handle force was used as model input, and EMG data from the external oblique muscles was compared with simulation output. Model coefficients were optimized by a least-squares algorithm. The optimization produced a good similarity between simulation and experimental data with determination coefficients of r(2)=0.7 and greater. Furthermore, as a model validation, the model coefficients were used to predict other perturbation trials with similarities between predictions and respective EMG data of about r(2)=0.45, which was in the range of trial-to-trial EMG variability. The observed oscillations are assumed to originate from the central nervous system with changes in the neural circuitries between trials. Hence, the oscillations in single trial responses which are usually regarded as noise might be generated by the dynamics of a neural oscillator.