通过阈值自适应让脉冲网络找到最短路径并区分相似位置
Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation
- 用脉冲时序调节阈值实现活动反向追踪
- 训练后用更少重播次数计算出最短路径
- 适合做类脑导航与定位的神经形态系统
高效空间导航是哺乳动物大脑的特征，启发了模仿生物原理的神经形态系统发展。尽管已有进展，但如何在类脑脉冲神经网络中实现回溯和处理模糊性仍面临挑战。本文提出一种在任意单向脉冲神经元图中进行活动回溯的机制，通过脉冲时序依赖的阈值自适应（STDTA）扩展了脉冲分层时间记忆（S-HTM）的重放机制，使脉冲网络可实现路径规划。进一步提出依赖模糊性的阈值自适应（ADTA），在环境相似时降低定位模糊性，提升智能体的定位精度。结合两种方法，网络能高效识别通往无歧义目标的最短路径。实验表明，经过序列训练的网络以少于到达目标所需步数的重播次数可靠计算出最短路径，并可在多个相似环境中减少模糊性以识别位置。这些成果推动了类脑序列学习算法如S-HTM在神经形态定位与导航中的实际应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。