Journal
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
Volume 5, Issue 5, Pages 743-754Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETCI.2020.3005703
Keywords
Memristors; Neurons; Synapses; Training; Computer architecture; Biological neural networks; Integrated circuit modeling; Artificial neural network; bidirectional associative memory; memristor; synaptic weight; circuit design
Categories
Funding
- National Natural Science Foundation of China [61936004, 61673188]
- Innovation Group Project of the National Natural Science Foundation of China [61821003]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
- 111 Project on Computational Intelligence and Intelligent Control [B18024]
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A novel network circuit based on memristor synapses is proposed for bidirectional associative memory with in-situ learning method, featuring advantages such as analog neuron circuit to emulate neural networks' activation function and adaptation to memristor's nonlinear characteristics.
Memristor is considered as a promising synaptic device for neural networks because of its tunable and non-volatile resistance states, which is similar to the biological synapses. In this article, a novel network circuit based on memristor synapses is proposed for bidirectional associative memory with in-situ learning method. An analog neuron circuit is designed to emulate the cubic activation function of neural networks. A memristive synapse circuit is constructed to map both positive and negative weights on a single memristor. Moreover, an in-situ learning circuit fitting memristor's nonlinear characteristic is proposed. Feedback control strategy is incorporated in this learning circuit to adjust the resistance of the memristor and avoid the encoding error of the memristor's write voltage. The performance of the proposed network circuit is verified by the training and recalling simulations. The comparison between the proposed approach and related works is analyzed to demonstrate the advantage of the proposed circuit design.
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