COMPUTER SIMULATION STUDY ON CENTRAL PATTERN GENERATOR: FROM BIOLOGY TO ENGINEERING

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation

OBJECTIVE

A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom.

METHODS

We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection.

RESULTS

The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor.

CONCLUSION

The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics.

SIGNIFICANCE

Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.

BURSTING NEAR BAUTIN BIFURCATION IN A NEURAL NETWORK WITH DELAY COUPLING

Bursting behavior is one of the most important firing activities of neural system and plays an important role in signal encoding and transmission. In the present paper, a neural network with delay coupling is modeled to investigate the generation mechanism of bursting behavior. The Andronov-Hopf bifurcation is firstly studied and then the degenerated Andronov-Hopf bifurcation, namely Bautin bifurcation, is analyzed with the external input varying. Classifying dynamics in the neighborhood of the Bautin bifurcation, we obtain the bifurcation sets where the supercritical/subcritical Andronov-Hopf, or the fold limit cycle bifurcation may happen in the system under consideration. For a periodic disturbance occurring in the neighborhood of the Bautin bifurcation, it is seen that the Andronov-Hopf bifurcation and fold limit cycle bifurcation may lead to the transition from quiescent state to firing activities. Complex bursting phenomena, including Hopf/Hopf bursting, Hopf/Fold cycle bursting, SubHopf/Hopf bursting and SubHopf/Fold cycle bursting are found in the firing area. The results show that the dynamical properties of different burstings are related to the dynamical behaviors derived from the bifurcations of the system. Finally, it is seen that the bursting disappears but the periodic spiking appears in the delayed neural network for large values of delay.

MATHEMATICAL STUDY ON IONIC MECHANISM OF LAMPREY CENTRAL PATTERN GENERATOR MODEL

This paper studies the mechanisms of ionic channels in neurons of lamprey central pattern generator (CPG), such as the N-methyl-D-aspartate (NMDA) receptor channel and the calcium-dependent potassium channel etc. The CPG properties on oscillation attributed to the ionic mechanisms are exploited. The conditions for oscillation, divergence, convergence and the guidelines on selection of the parameters are established. The effects of key parameters on CPG frequency and duty cycle are investigated. Mathematical analysis and simulation study is performed to verify these results. This study will potentially enhance the effective application of biological CPG model into engineering practice such as robotics.

THEORETICAL ANALYSIS ON NEURAL OSCILLATOR TOWARD BIOMIMIC ROBOT CONTROL

Neural oscillator is derived from the central pattern generator (CPG) in the biological nervous system. It can generate motor patterns for the rhythmic movements. Neural oscillator is widely adopted in biomimic robot and humanoid robot for different types of rhythmic movement controls such as swimming and walking. Theoretical analysis about neural oscillator toward biomimic robot control is presented in this paper. The methods adopted here include stability theory, describing function, and piecewise linear analysis. Some important properties of the neural oscillator, such as the determination of frequency, oscillation, and stability, are exploited. Network property of multiple neural oscillators is also studied. The insightful results will strengthen the foundation of the neural oscillator and enhance its efficient application for robotic control purpose.