The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capability constructed by the Continuous Attractor Neural Networks (CANNs) in most existing BIN studies exhibits significant computational redundancy, and its operational efficiency needs to be improved; otherwise, it will not be conducive to the practicality of BIN technology. To address this, this paper proposes an efficient PI approach using representation learning models to replicate CANN neurodynamic patterns. This method successfully replicates the neurodynamic patterns of CANN-modeled Head Direction Cells (HDCs) and Grid Cells (GCs) using lightweight Artificial Neural Networks (ANNs). These ANN-reconstructed HDC and GC models are then integrated to achieve brain-inspired PI for Dead Reckoning (DR). Benchmark tests in various environments, compared with the well-known NeuroSLAM system, demonstrate that this work not only accurately replicates the neurodynamic patterns of navigation cells but also matches NeuroSLAM in positioning accuracy. Moreover, efficiency improvements of approximately 17.5% on the general-purpose device and 40~50% on the edge device were observed, compared with NeuroSLAM. This work offers a novel implementation strategy to enhance the practicality of BIN technology and holds potential for further extension.

翻译：大脑的路径整合机制为类脑导航提供了重要的指导和启发。然而，现有大多数类脑导航研究中基于连续吸引子神经网络构建的路径整合能力存在显著的计算冗余，其运行效率有待提升，否则将不利于类脑导航技术的实用化。为此，本文提出一种利用表征学习模型复现连续吸引子神经动力学模式的高效路径整合方法。该方法使用轻量级人工神经网络成功复现了连续吸引子网络建模的头部方向细胞与网格细胞的神经动力学模式。这些经人工神经网络重构的头部方向细胞与网格细胞模型随后被整合，以实现用于航位推算的类脑路径整合。在不同环境中的基准测试表明，与知名的NeuroSLAM系统相比，本工作不仅准确复现了导航细胞的神经动力学模式，且在定位精度上与NeuroSLAM相当。此外，相较于NeuroSLAM，在通用设备上观察到约17.5%的效率提升，在边缘设备上观察到40~50%的效率提升。本工作为增强类脑导航技术的实用性提供了一种新颖的实现策略，并具有进一步扩展的潜力。