Abstract
In this paper, we propose an unsupervised HMAX-based Spiking Deep Neural Network (HMAX-SDNN) for object recognition. HMAX is a biologically plausible model based on the hierarchical activity of object recognition in visual cortex. In HMAX-SDNN, input layer with HMAX structure is followed by a stacked convolution-pooling structure, in which convolutional layers are hierarchically trained with STDP. After that, a linear SVM is used for classification. Then, we demonstrate that the firing threshold has positive correlation with receptive fields size in convolutional layers, and optimize HMAX-SDNN with this conclusion. With the optimized structure, we validate HMAX-SDNN on Caltech dataset, and HMAX-SDNN outperforms other SNNs by reaching 99.2% recognition accuracy. Furthermore, the experiments show that HMAX-SDNN is robust to different kinds of objects.
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This work was supported by the National Key R&D Program of China under Grant 2017YFB1300201 and the National Natural Science Foundation of China under Grant 61673283.
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Song, Z., Wu, X., Yuan, M., Tang, H. (2019). An Unsupervised Spiking Deep Neural Network for Object Recognition. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_36
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