Memristor-based circuit design of interweaving mechanism of emotional memory in a hippocamp-brain emotion learning model

IF 6.3 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2025-06-01 Epub Date: 2025-02-13 DOI:10.1016/j.neunet.2025.107276
Yunlai Zhu, Yongjie Zhao, Junjie Zhang, Xi Sun, Ying Zhu, Xu Zhou, Xuming Shen, Zuyu Xu, Zuheng Wu, Yuehua Dai
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Abstract

Endowing robots with human-like emotional and cognitive abilities has garnered widespread attention, driving deep investigations into the complexities of these processes. However, few studies have examined the intricate circuits that govern the interplay between emotion and memory. This work presents a memristive circuit design that generates emotional memory, mimicking human emotional responses and memories while enabling interaction between emotions and cognition. Leveraging the hippocampal-brain emotion learning (BEL) architecture, the memristive circuit comprises seven comprehensive modules: the thalamus, sensory cortex, orbitofrontal cortex, amygdala, dentate gyrus (DG), CA3, and CA1. This design incorporates a compact biological framework, facilitating the collaborative encoding of emotional memories by the amygdala and hippocampus and allowing for flexible adjustment of circuit parameters to accommodate diverse personality traits. The proposed memristor-based circuit effectively mimics the complex interplay between emotions and memory, providing a valuable foundation for advancing the development of robots capable of replicating human-like emotional responses and cognitive integration.
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海马-脑情绪学习模型中情绪记忆交织机制的忆阻器电路设计
赋予机器人类似人类的情感和认知能力已经引起了广泛关注,推动了对这些过程复杂性的深入研究。然而,很少有研究考察控制情绪和记忆之间相互作用的复杂回路。这项工作提出了一个记忆电路设计,产生情绪记忆,模仿人类的情绪反应和记忆,同时使情绪和认知之间的互动。利用海马体-大脑情感学习(BEL)结构,记忆回路包括七个综合模块:丘脑、感觉皮层、眶额皮质、杏仁核、齿状回(DG)、CA3和CA1。这种设计结合了一个紧凑的生物框架,促进了杏仁核和海马体对情感记忆的协同编码,并允许灵活调整电路参数以适应不同的人格特征。所提出的基于记忆电阻的电路有效地模拟了情感和记忆之间复杂的相互作用,为推进能够复制人类情感反应和认知整合的机器人的发展提供了有价值的基础。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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