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An Unsupervised Spiking Deep Neural Network for Object Recognition

  • Zeyang Song
  • Xi Wu
  • Mengwen Yuan
  • Huajin TangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

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.

Keywords

HMAX Spiking Deep Neural Network STDP Deep learning Object recognition 

Notes

Acknowledgements

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Neuromorphic Computing Research Center, College of Computer ScienceSichuan UniversityChengduChina

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