The image principal contour contains important image features, and an accurate and effective extraction method of the image principal contour can not only reduce information redundancy but also reduce the time complexity of subsequent image analysis and processing. Depending on the information processing mechanism of visual neurons, this paper proposes an image principal contour extraction method based on spatio-temporal spike coding. First, the Gabor function is used to simulate the multi-scale and multi-directional information extraction from the image by the receptive field of ganglion cells. Second, the non-classical receptive field of the retina is simulated to construct an anisotropic suppression model to suppress the edge of the image background texture. Then, as for the visual images obtained by the receptive field of different scales, the small scale of the visual receptive field can help extract most of the texture information of the image, and the extraction under the large scale can make most of the texture of the image disappear, with only some characteristics of the principal contour retained for the adaptive adjustment of weights to perform spatio-temporal spike coding. Finally, the leaky integrate-and-fire neuron model is used to extract the principal contour of the image, and the principal contour of the final image is obtained by non-maximum suppression and hysteresis threshold binarization. From both subjective and objective aspects, the method proposed in this paper is simulated and verified on the RUG40 database, and its performance is compared with that of the existing mainstream image contour extraction methods. The experimental results show that the proposed method can reduce the redundant information of the principal contour of the image while effectively improving the accuracy of principal contour extraction.