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Add-weighted one-rank local-region multi-steps forecasting model (AOLMM) is adopted to predict the neuron spikes of MPTP monkey model of ParkinsonÂ¿s disease (PD).The AOLMM based on Takens embedding theory has been proved as effectively predict many chaotic systems and overcome some shortcomings like Large computational quantity and cumulative error of other chaotic prediction methods. Many previous studies have demonstrated the existence of certain neurons in the thalamus of PD patients especially in the Globus Pallidus(GP) is closely related with the pathogenesis of tremor. We observed that with appropriate embedding dimension and the proper maximum forecasting step, the AOLMM can well foretell the dynamical trend of the GP neuron spikes of the MPTP induced monkey model of PD. It indicates that AOLMM is powerful to help us understand the pathological mechanism of PD better and clear.