| SPECIAL TOPIC — Biophysical circuits: Modeling & applications in neuroscience |
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Environmental-gradient emotional memory memristive neural network circuit with TAP cell regulatory mechanism |
| Peng Qin(秦鹏)1, Tieqiao Liu(刘铁桥)2, Qiuzhen Wan(万求真)1,†, Rou Zhou(周柔)1, and Huaimin Xiang(向怀民)1 |
1 College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China; 2 School of Information, Zhejiang University of Finance and Economics Dongfang College, Haining 314408, China |
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Abstract The hippocampus and amygdala in the human brain play a crucial role in the processing of emotion and memory. Specifically, the hippocampus encodes environmental information along with its corresponding emotional states, whereas the amygdala integrates emotional stimuli from the environment into the hippocampal memory system. Inspired by this biological mechanism and based on the principle of regulating cellular excitability within the mouse transient amplifying progenitor (TAP) mechanism, this study proposes an environmental-gradient emotional memory memristive neural network circuit. This bio-inspired neuromorphic circuit consists of two main modules: a hippocampal module and an amygdala module. The hippocampal module comprises two sub-modules: the environmental recognition neuron and the emotion generation neuron. The environmental recognition neuron is responsible for memorizing environmental features, while the emotion generation neuron establishes mapping relationships between the environment and emotional states. The amygdala module combines external emotional stimuli with internal current emotional stimuli to generate a comprehensive emotional assessment of the environment, and this emotional state can be stored. This memristive neural network circuit facilitates dynamic coupling between emotion and the environment, with the emotional output being continuous and graded rather than discrete. In PSPICE simulations, the proposed circuit exhibits satisfactory and stable functional performance. The findings of this study can offer valuable insights for the design of neuromorphic hardware circuits and for emotion simulation in bio-inspired robots.
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Received: 24 January 2026
Revised: 06 March 2026
Accepted manuscript online: 10 March 2026
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PACS:
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87.19.ll
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(Models of single neurons and networks)
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87.19.lv
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(Learning and memory)
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87.19.lw
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(Plasticity)
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84.35.+i
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(Neural networks)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61901169 and 61804037) and the Natural Science Foundation of Hunan Province (Grant No. 2024JJ5267). |
Corresponding Authors:
Qiuzhen Wan
E-mail: wanqiuzhen@sina.com
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Cite this article:
Peng Qin(秦鹏), Tieqiao Liu(刘铁桥), Qiuzhen Wan(万求真), Rou Zhou(周柔), and Huaimin Xiang(向怀民) Environmental-gradient emotional memory memristive neural network circuit with TAP cell regulatory mechanism 2026 Chin. Phys. B 35 068709
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[1] Pandey A K and Gelin R 2018 IEEE Robot. Autom. Mag. 25 40 [2] Dou G, Li D G, Guo M and Herbert H I 2025 IEEE Trans. Circuits Syst. I Regul. Pap. 72 2566 [3] He W, Li Z J and Chen C L P 2017 IEEECAA J. Autom. Sin. 4 602 [4] Ma J 2025 Nonlinear Dyn. 113 25365 [5] Wan Q Z, Liu J, Liu T Q, Sun K L and Qin P 2024 Neural Netw. 174 106268 [6] Justinas M, Pietro C, Patricia D, et al. 2020 IEEE Robot. Autom. Lett. 5 5339 [7] Jiang X, Nie J L, Fan D G and Duan L X 2025 Chin. Phys. B 34 128702 [8] Croissant M, Frister M, Schofield G and McCall C 2024 PLoS One 19 e0301033 [9] Rawal N and Maria R S H 2022 Int. J. Soc. Robot. 14 1583 [10] Liu Z T, Wu M, Cao W H, et al. 2017 IEEECAA J. Autom. Sin. 4 668 [11] Rincon J A, Costa A, Novais P, Julian V and Carrascosa C 2019 Knowl. Inf. Syst. 60 363 [12] Sun Z, Kvatinsky S, Si X, Mehonic A, Cai Y and Huang R 2023 Nat. Electron. 6 823 [13] Sebastian A, Gallo M L, Khaddam-Aljameh R and Eleftheriou E 2020 Nat. Nanotechnol. 15 529 [14] Yang C,Wang X P, Chen Z F, et al. 2025 IEEE Trans. Autom. Sci. Eng. 22 4501 [15] Zhu Y L, Zhao Y J, Zhang J J, et al. 2025 Neural Netw. 186 107276 [16] Yuan F, Yu X C, Deng Y, Li Y X and Chen G R 2024 IEEE Trans. Ind. Electron. 71 9480 [17] Li F Y, Tang H G, Zhang Y Z, Bao B C, Hassanin H and Bai L F 2025 Chin. Phys. B 34 128701 [18] Mou J, Cao H L, Zhou N R and Cao Y H 2024 IEEE Trans. Cybern. 54 7333 [19] Zhou X W, Jiang D H, Nkapkop J D D, et al. 2024 Chin. Phys. B 33 040506 [20] Li X X, He Q Q, Yu T Y, Cai Z and Xu G Z 2024 Chin. Phys. B 33 030505 [21] Aguirre F, Sebastian A, Gallo M L, et al. 2024 Nat. Commun. 15 1974 [22] Guo W B, Feng Z, Wang H C, et al. 2025 Chin. Phys. B 34 127301 [23] Deng Q L,Wang C H, Sun Y C, et al. 2025 IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 44 4701 [24] Sun J W, Zhao L H, Wen S P and Wang Y F 2022 Electronics 11 3851 [25] Sun J W, Gao P L, Liu P and Wang Y F 2025 IEEE Trans. Ind. Inf. 21 5633 [26] Wan Q Z, Sun K L, Liu T Q and Qin P 2025 Int. J. Circuit Theory Appl. 53 4890 [27] Sun J W, Zhai Y, Liu P, et al. 2025 IEEE Trans. Neural Netw. Learn. Syst. 36 3618 [28] Sun J W, Zhao Y, Wang Y F, et al. 2024 IEEE Trans. Circuits Syst. II Express Briefs 71 4601 [29] Miyashita S and Hoshino M 2022 Cells 11 726 [30] Marymonchyk A, Rodriguez-Aller R, Willis A, et al. 2025 Cell Stem Cell 32 445 [31] Kvatinsky S, Friedman E G, Kolodny A and Weiser U C 2013 IEEE Trans. Circuits Syst. I Regul. Pap. 60 211 [32] Kvatinsky S, Ramadan M, Friedman E G and Kolodny A 2015 IEEE Trans. Circuits Syst. II Express Briefs 62 786 [33] Li Y, Xie L J, Xiao P D, Zheng C Y and Hong Q H 2023 Neural Comput. Appl. 35 14419 [34] Sun J W, Cao Y H, Yue Y, Wang Y and Wang Y F 2025 IEEE Internet Things J. 12 4158 [35] Sun JW, Tao K F,Wen S P,Wang Z C andWang Y F 2025 IEEE Trans. Cybern. 55 5518 [36] Hong Q H, Jiang H Y, Xiao P D, Du S C and Li T 2025 IEEE Trans. Comput. 74 996 [37] Ma M L, Yuan Z Y and Zhao X 2026 Nonlinear Dyn. 114 275 [38] Li Z J and Zhang J 2024 Chin. Phys. B 33 128701 |
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