{"title":"A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex","authors":"Zihui Tang;Xiaoping Wang;Chao Yang;Zhanfei Chen;Zhigang Zeng","doi":"10.1109/TBCAS.2024.3350135","DOIUrl":null,"url":null,"abstract":"In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a ‘one-hot’ code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.","PeriodicalId":94031,"journal":{"name":"IEEE transactions on biomedical circuits and systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biomedical circuits and systems","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10381810/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a ‘one-hot’ code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.
本文提出了一种仿生定位忆阻器电路,主要由头部方向单元模块、网格单元模块、位置单元模块和解码模块组成。这项工作将网格单元的二维连续吸引子网络(CAN)模型修改为 X 和 Y 方向的两个一维模型。头部方向单元模块利用忆阻器来整合角速度,并代表代理的真实方向。网格单元模块利用忆阻器感知线速度和方向信号,这两种信号都是自我运动线索,并通过周期性发射模式对空间位置进行编码。位置单元模块接收网格单元模块的输出,并在特定位置点火。解码模块对角度或位置信息进行解码,并将神经元状态转换为 "单击 "代码。该电路完成了空间定位任务,并通过使用忆阻器实现了内存计算,从而缩短了执行时间。上述功能在 LTSPICE 中实现。仿真结果表明,所提出的电路可以实现路径集成和定位。此外,仿真结果还表明,所提出的电路具有良好的鲁棒性和较低的面积开销。这项工作为前瞻性机器人平台提供了一种可能的应用思路,以帮助机器人定位和构建地图。