Small-world Hopfield neural networks with weight salience priority and memristor synapses for digit recognition |
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Authors: | Shukai Duan Zhekang Dong Xiaofang Hu Lidan Wang Hai Li |
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Affiliation: | 1.School of Electronics and Information Engineering,Southwest University,Chongqing,China;2.Department of Mechanical and Biomedical Engineering,City University of Hong Kong,Kowloon,Hong Kong;3.Department of Electrical and Computer Engineering,University of Pittsburgh,Pittsburgh,USA |
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Abstract: | A novel systematic design of associative memory networks is addressed in this paper, by incorporating both the biological small-world effect and the recently acclaimed memristor into the conventional Hopfield neural network. More specifically, the original fully connected Hopfield network is diluted by considering the small-world effect, based on a preferential connection removal criteria, i.e., weight salience priority. The generated sparse network exhibits comparable performance in associative memory but with much less connections. Furthermore, a hardware implementation scheme of the small-world Hopfield network is proposed using the experimental threshold adaptive memristor (TEAM) synaptic-based circuits. Finally, performance of the proposed network is validated by illustrative examples of digit recognition. |
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