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：大脑的路径积分机制为脑启发导航提供了重要指导和启示。然而，现有大多数脑启发导航研究中由连续吸引子神经网络构建的路径积分能力存在显著计算冗余，其运算效率亟待提升，否则将不利于脑启发导航技术的实用性。为此，本文提出一种利用表征学习模型复制连续吸引子神经网络神经动力学模式的高效路径积分方法。该方法成功使用轻量级人工神经网络复制了连续吸引子神经网络建模的头朝向细胞和网格细胞的神经动力学模式。随后，将这些人工神经网络重建的头朝向细胞和网格细胞模型集成，实现用于航位推算的脑启发路径积分。在不同环境下的基准测试中，与知名NeuroSLAM系统相比，本工作不仅精确复制了导航细胞的神经动力学模式，而且在定位精度上与NeuroSLAM相当。此外，在通用设备上观察到效率提升约17.5%，在边缘设备上观察到效率提升约40%至50%。本工作为提升脑启发导航技术实用性提供了一种新的实现策略，并具有进一步扩展的潜力。