DocumentCode
1797550
Title
STDP learning rule based on memristor with STDP property
Author
Ling Chen ; Chuandong Li ; Tingwen Huang ; Xing He ; Hai Li ; Yiran Chen
Author_Institution
Coll. of Comput., Chongqing Univ., Chongqing, China
fYear
2014
fDate
6-11 July 2014
Firstpage
1
Lastpage
6
Abstract
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.
Keywords
memristors; neural nets; MNN; STDP learning ability; STDP learning rule; STDP property; memritive neural network; physical memristors; simplified neurons; spike timing dependent plasticity; Analytical models; Biological neural networks; Educational institutions; Electric potential; Memristors; Multi-layer neural network; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-6627-1
Type
conf
DOI
10.1109/IJCNN.2014.6889506
Filename
6889506
Link To Document