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Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses

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Abstract

A memrsitor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. In this paper, we present a class of memristor-based neural circuits comprising leaky integrate-and-fire (I & F) neurons and memristor-based learning synapses. Employing these neuron circuits and corresponding SPICE models, the properties of a two neurons network are shown to be similar to biology. During correlated spiking of the pre- and post-synaptic neurons, the strength of the synaptic connection increases. Conversely, it is diminished when the spiking is uncorrelated. This synaptic plasticity and associative learning is essential for performing useful computation and adaptation in large scale artificial neural networks. Finally, future circuit design and consideration are discussed with the memristor-based neural networks.

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Acknowledgments

The work is supported by the Natural Science Foundation of China under Grant 60974021, the 973 Program of China under Grant 2011CB710606, the Fund for Distinguished Young Scholars of Hubei Province under Grant 2010CDA081, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant 20100142110021, National Priority Research Project NPRP 4-451-2-168, funded by Qatar National Research Fund.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Shiping Wen & Zhigang Zeng

  2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan, 430074, China

    Shiping Wen & Zhigang Zeng

  3. Texas A & M University at Qatar, Doha, 5825, Qatar

    Tingwen Huang

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  1. Shiping Wen
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  2. Zhigang Zeng
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  3. Tingwen Huang
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Correspondence to Zhigang Zeng.

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Wen, S., Zeng, Z. & Huang, T. Associative Learning of Integrate-and-Fire Neurons with Memristor-Based Synapses. Neural Process Lett 38, 69–80 (2013). https://doi.org/10.1007/s11063-012-9263-8

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