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STDP learning rule based on memristor with STDP property

Publication ,  Conference
Chen, L; Li, C; Huang, T; He, X; Li, H; Chen, Y
Published in: Proceedings of the International Joint Conference on Neural Networks
September 3, 2014

Spike-timing-dependent plasticity (STDP) learning ability has been observed in physical memristors, but whether the STDP is caused by the neuron or the memristor is unclear. In this paper, we proved the STDP property in the model for both symmetric and asymmetric memristor. We also employed the symmetric/asymmetric memristors with STDP property and the simplified neurons to perform the STDP learning ability. At last, the sequence learning experiment of the memritive neural network (MNN) with the symmetric memristor synapse further verifies the STDP learning ability of the memristor.

Duke Scholars

Published In

Proceedings of the International Joint Conference on Neural Networks

DOI

Publication Date

September 3, 2014

Start / End Page

1 / 6
 

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Chen, L., Li, C., Huang, T., He, X., Li, H., & Chen, Y. (2014). STDP learning rule based on memristor with STDP property. In Proceedings of the International Joint Conference on Neural Networks (pp. 1–6). https://doi.org/10.1109/IJCNN.2014.6889506
Chen, L., C. Li, T. Huang, X. He, H. Li, and Y. Chen. “STDP learning rule based on memristor with STDP property.” In Proceedings of the International Joint Conference on Neural Networks, 1–6, 2014. https://doi.org/10.1109/IJCNN.2014.6889506.
Chen L, Li C, Huang T, He X, Li H, Chen Y. STDP learning rule based on memristor with STDP property. In: Proceedings of the International Joint Conference on Neural Networks. 2014. p. 1–6.
Chen, L., et al. “STDP learning rule based on memristor with STDP property.” Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 1–6. Scopus, doi:10.1109/IJCNN.2014.6889506.
Chen L, Li C, Huang T, He X, Li H, Chen Y. STDP learning rule based on memristor with STDP property. Proceedings of the International Joint Conference on Neural Networks. 2014. p. 1–6.

Published In

Proceedings of the International Joint Conference on Neural Networks

DOI

Publication Date

September 3, 2014

Start / End Page

1 / 6