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In order to improve the accuracy of human action recognition and accelerate the recognition speed, we propose a method for human action recognition by modeling the human primary visual cortex neurons. The method firstly extracted motion information by using 3DGabor spatial-temporal filters to model the classical receptive field (CRF) of simple cells in the primary visual cortex. Secondly, conductance-driven integrate and fire neuron model was used to simulate the primary visual cortex neuron, by which motion information was converted into spike train. Finally, the mean firing rate of spike train formed a feature vector that captures the characteristic of human actions in this video sequence. Using Support Vector Machine (SVM), the method is tested on the Weizmann action dataset. The obtained impressive results show that our method was more effective than model of Escobar in human action recognition.