A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex

• Published in: IEEE transactions on biomedical circuits and systems Vol. 18; no. 3; pp. 552 - 563
• Main Authors: Tang, Zihui, Wang, Xiaoping, Yang, Chao, Chen, Zhanfei, Zeng, Zhigang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angular velocity; Bio-inspired computing; Bionics; Bionics - instrumentation; Cell culture; circuit implementation; Cognition - physiology; Computer Simulation; Cortex (entorhinal); Decoding; Entorhinal Cortex - physiology; Firing; Hippocampus; Hippocampus - physiology; Humans; Integrated circuit modeling; Localization; Location awareness; Memristor; Memristors; Mental task performance; Models, Neurological; Modules; Navigation; Neural Networks, Computer; Neurons; One dimensional models; Robots; spatial cells; spatial cognitive mechanism; Velocity
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2024.3350135

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.