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Wang C, Xu C, Sun J, Deng Q. A memristor-based associative memory neural network circuit with emotion effect. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08275-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Dong S, Chen Y, Fan Z, Chen K, Qin M, Zeng M, Lu X, Zhou G, Gao X, Liu JM. A backpropagation with gradient accumulation algorithm capable of tolerating memristor non-idealities for training memristive neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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A memristor-based circuit design and implementation for blocking on Pavlov associative memory. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07162-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Memristor-based affective associative memory neural network circuit with emotional gradual processes. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07170-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Pei M, Wan C, Chang Q, Guo J, Jiang S, Zhang B, Wang X, Shi Y, Li Y. A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem. RESEARCH (WASHINGTON, D.C.) 2021; 2021:9820502. [PMID: 35024616 PMCID: PMC8715308 DOI: 10.34133/2021/9820502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/14/2021] [Indexed: 12/21/2022]
Abstract
Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals' adaptability. However, enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment, ideally in a single device, is challenging. Here, we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative learning. This approach couples the photogating effect at the interface with ferroelectric polarization switching, enabling highly tunable optical modulation of charge carriers. Our device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to two. In particular, we obtain a large output difference (>103), which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless conduction. As proof-of-concept demonstrations, photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device, indicating compatibility with biological and digital data processing.
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Affiliation(s)
- Mengjiao Pei
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Changjin Wan
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Qiong Chang
- School of Computing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
| | - Jianhang Guo
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Sai Jiang
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China
| | - Bowen Zhang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Xinran Wang
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Yi Shi
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
| | - Yun Li
- National Laboratory of Solid-State Microstructures, School of Electronic Science and Engineering, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
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Li Y, Li J, Li J, Duan S, Wang L, Guo M. A reconfigurable bidirectional associative memory network with memristor bridge. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.077] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Sun J, Han J, Wang Y, Liu P. Memristor-Based Neural Network Circuit of Emotion Congruent Memory With Mental Fatigue and Emotion Inhibition. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:606-616. [PMID: 34156947 DOI: 10.1109/tbcas.2021.3090786] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Most memristor-based neural networks only consider a single mode of memory or emotion, but ignore the relationship between emotion and memory. In this paper, a memristor-based neural network circuit of emotion congruent memory is proposed and verified by the simulation results. The designed circuit consists of a memory module, an emotion module and an association neuron module. Varieties of memory and emotion functions are considered. The functions such as learning, forgetting, variable rate and emotion generation are implemented by the circuit. Furthermore, mental fatigue and emotion inhibition which are two important self-protective measures of the brain are realized in this paper on the basis of emotion congruent memory. Finally, the paper also considers the congruence between emotion and memory materials and the regulation of emotion on memory. The neural network circuit of emotion congruent memory can provide more references for the application of memristor.
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