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Memristor-based affective associative memory neural network circuit with emotional gradual processes

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Abstract

In the existing affective associative memory neural network circuits, the change of emotions in the affective associative learning and forgetting processes is abrupt and the intensity of emotions is invariable. In fact, the transition from one emotion to another is a gradual process. In this paper, to realize the progressive changes of emotional intensity in the affective associative memory neural network, the gradual learning, gradual forgetting and gradual transferring processes of emotions are proposed and the memristor-based circuit of the affective associative memory neural network is designed. In the designed circuit, the firing frequency of output neurons is closely correlated with the intensity of emotions. The higher the firing frequency of output neurons, the stronger the emotional intensity. Based on the associative memory rule, the dynamical change of the synaptic weights leads to the gradual variation of the frequencies of output neurons. Thus, the function of variable emotional intensity can be realized and the gradual processes can be achieved. The PSPICE simulation results are given to verify that the proposed circuit could realize the affective learning, forgetting and transferring functions with gradual processes.

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Author information

Author notes

  1. Meiling Liao

    Present address: College of Information Science and Engineering, Hunan University, Changsha, 410082, China

Authors and Affiliations

  1. College of Information Science and Engineering, Hunan University, Changsha, 410082, China

    Chunhua Wang, Hairong Lin & Cong Xu

  2. School of Engineering and Computer Science, University of Hertfordshire, Hatfield, AL109AB, UK

    Yichuang Sun

Authors
  1. Meiling Liao
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  2. Chunhua Wang
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  3. Yichuang Sun
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  4. Hairong Lin
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  5. Cong Xu
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Correspondence to Chunhua Wang.

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Liao, M., Wang, C., Sun, Y. et al. Memristor-based affective associative memory neural network circuit with emotional gradual processes. Neural Comput & Applic 34, 13667–13682 (2022). https://doi.org/10.1007/s00521-022-07170-z

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