Efficient spatial navigation is a hallmark of the mammalian brain, inspiring the development of neuromorphic systems that mimic biological principles. Despite progress, implementing key operations like back-tracing and handling ambiguity in bio-inspired spiking neural networks remains an open challenge. This work proposes a mechanism for activity back-tracing in arbitrary, uni-directional spiking neuron graphs. We extend the existing replay mechanism of the spiking hierarchical temporal memory (S-HTM) by our spike timing-dependent threshold adaptation (STDTA), which enables us to perform path planning in networks of spiking neurons. We further present an ambiguity dependent threshold adaptation (ADTA) for identifying places in an environment with less ambiguity, enhancing the localization estimate of an agent. Combined, these methods enable efficient identification of the shortest path to an unambiguous target. Our experiments show that a network trained on sequences reliably computes shortest paths with fewer replays than the steps required to reach the target. We further show that we can identify places with reduced ambiguity in multiple, similar environments. These contributions advance the practical application of biologically inspired sequential learning algorithms like the S-HTM towards neuromorphic localization and navigation.

翻译：高效的空间导航是哺乳动物大脑的显著特征，这启发了模拟生物原理的神经形态系统的开发。尽管取得了进展，但在仿生脉冲神经网络中实现回溯和处理模糊性等关键操作仍然是一个开放挑战。本研究提出了一种在任意单向脉冲神经元图中进行活动回溯的机制。我们通过提出的脉冲时间依赖阈值适应（STDTA）扩展了现有脉冲层次时序记忆（S-HTM）的回放机制，从而能够在脉冲神经元网络中进行路径规划。我们进一步提出了一种模糊性依赖阈值适应（ADTA），用于在模糊性较低的环境中识别位置，从而增强智能体的定位估计。这些方法相结合，能够高效识别到达明确目标的最短路径。实验表明，在序列上训练的网络能够可靠地计算出最短路径，且所需回放次数少于到达目标所需的步数。我们还证明，可以在多个相似环境中识别模糊性降低的位置。这些贡献推动了如S-HTM等仿生序列学习算法在神经形态定位与导航中的实际应用。