用轻量神经网络复现脑导航神经动力学，提升路径积分效率。
Boosting Brain-inspired Path Integration Efficiency via Learning-based Replication of Continuous Attractor Neurodynamics
- 用学习方法复制连续吸引子神经网络的动态模式
- 在通用设备上提速17.5%，边缘设备上提速40%~50%
- 适合追求高效脑启发导航的机器人与嵌入式系统研究者
大脑的路径积分（PI）机制为脑启发导航（BIN）提供了重要参考。然而，现有大部分BIN研究中基于连续吸引子神经网络（CANN）构建的PI能力存在显著计算冗余，运行效率有待提升，否则难以支持实际应用。为此，本文提出一种基于表示学习模型的高效PI方法，通过轻量级人工神经网络（ANN）复现CANN建模的头方向细胞（HDC）和网格细胞（GC）的神经动力学模式。重构后的ANN-HDC与ANN-GC模型被集成用于死记导航（DR）的脑启发路径积分。在多种环境下的基准测试表明，该方法不仅准确复现了导航细胞的神经动力学特性，且定位精度与知名NeuroSLAM系统相当。相比NeuroSLAM，本方法在通用设备上效率提升约17.5%，在边缘设备上提升40%~50%。该工作为提升BIN技术实用性提供了新思路，具备进一步扩展潜力。
原文摘要 · Abstract (English)
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.
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