| SPECIAL TOPIC — Biophysical circuits: Modeling & applications in neuroscience |
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Echolocation-inspired memristive behavioral decision circuit |
| Yueqi Song(宋钥淇)1, Xiaozhou He(何晓舟)2,†, Xianying Xu(徐宪莹)1, Yinghong Cao(曹颖鸿)1, Santo Banerjee3, Suo Gao(高锁)1, and Jun Mou(牟俊)1,‡ |
1 School of Information Science and Engineering, Dalian Polytechnic University, Dalian 116034, China;
2 School of Network Information Center, Dalian Medical University, Dalian 116044, China;
3 Department of Mathematical Sciences, Giuseppe Luigi Lagrange, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy |
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Abstract Memristive circuits have been widely employed to emulate various neural behavioral mechanisms. However, most existing works remain focused on isolated learning processes and have not yet established behavioral systems capable of adapting to changing stimuli, transitioning across perceptual states, and preserving behavioral continuity. To address these limitations, an echolocation-inspired memristive behavioral decision circuit is proposed in this work. The framework is organized into four functional modules — Stimulus, Action, Decision-making, and Memory — which operate cooperatively to enable behavioral generation that transitions from stimulus-driven responses to experience-driven execution across different conditions. Under strong stimulus conditions, behavior is directly elicited by external sensory input; under weak stimulus conditions, decision reliability is maintained through internal regulation; and under no-stimulus conditions, behavioral continuity is preserved through experience-based bias and memory replay. PSPICE simulations verify that the circuit maintains stable decision outputs and functional continuity across all conditions, demonstrating its effectiveness for biologically inspired and adaptive decision-making in neuromorphic systems.
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Received: 19 November 2025
Revised: 10 December 2025
Accepted manuscript online: 16 December 2025
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PACS:
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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07.50.-e
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(Electrical and electronic instruments and components)
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| Fund: This project was supported by the National Natural Science Foundation of China (Grant No. 62571079), the Technological Innovation Projects in the Field of Artificial Intelligence in Liaoning Province (Grant No. 2023JH26/10300011), the Basic Scientific Research Projects of the Department of Education of Liaoning Province (Grant No. LJ212410152049), and the Liaoning Provincial Science and Technology Plan Joint Project (Grant No. 2024-MSLH-033). |
Corresponding Authors:
Xiaozhou He, Jun Mou
E-mail: hexiaozhou@dmu.edu.cn;junmou@dlpu.edu.cn
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Cite this article:
Yueqi Song(宋钥淇), Xiaozhou He(何晓舟), Xianying Xu(徐宪莹), Yinghong Cao(曹颖鸿), Santo Banerjee, Suo Gao(高锁), and Jun Mou(牟俊) Echolocation-inspired memristive behavioral decision circuit 2026 Chin. Phys. B 35 060701
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