スパイキングネットワークの閾値適応による最短経路探索と場所の曖昧性解消
Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation
スパイキングニューラルネットワークにおいて、スパイクタイミング依存の閾値適応と曖昧性依存の閾値適応を導入し、経路のバックトレースと曖昧な環境での場所特定を可能にすることで、効率的な最短経路探索を実現した研究。
著者: Robin Dietrich, Tobias Fischer, Nicolai Waniek, Nico Reeb, Michael Milford, Alois Knoll, Adam D. Hines
分類: cs.NE, cs.AI, cs.RO
原文アブストラクト
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.
