# Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation

arXiv: https://arxiv.org/abs/2503.21795 · HTML: https://mlsanity.com/paper/2503.21795/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 85 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Fast and energy-efficient neuromorphic deep learning with first-spike times](https://mlsanity.com/paper/1912.11443/) (2019)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://mlsanity.com/paper/1511.00083/) (2015)
- [Sparse Spiking Gradient Descent](https://mlsanity.com/paper/2105.08810/) (2021)
- [Fully neuromorphic vision and control for autonomous drone flight](https://mlsanity.com/paper/2303.08778/) (2023)
- [Route Planning in Transportation Networks](https://mlsanity.com/paper/1504.05140/) (2015)
- [DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor](https://mlsanity.com/paper/2310.00564/) (2023)
- [Spiking Neural Network on Neuromorphic Hardware for Energy-Efficient Unidimensional SLAM](https://mlsanity.com/paper/1903.02504/) (2019)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness](https://mlsanity.com/paper/2107.07707/) (2021)
- [VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition](https://mlsanity.com/paper/2309.10225/) (2023)
- [Sequence learning, prediction, and replay in networks of spiking neurons](https://mlsanity.com/paper/2111.03456/) (2021)
- [Universal Optimality of Dijkstra via Beyond-Worst-Case Heaps](https://mlsanity.com/paper/2311.11793/) (2023)
- [Customizable Contraction Hierarchies](https://mlsanity.com/paper/1402.0402/) (2014)
- [Coherent noise enables probabilistic sequence replay in spiking neuronal networks](https://mlsanity.com/paper/2206.10538/) (2022)
- [Sequence learning in a spiking neuronal network with memristive synapses](https://mlsanity.com/paper/2211.16592/) (2022)
- [Neuromorphic Control using Input-Weighted Threshold Adaptation](https://mlsanity.com/paper/2304.08778/) (2023)
- [A Rapid Adapting and Continual Learning Spiking Neural Network Path Planning Algorithm for Mobile Robots](https://mlsanity.com/paper/2404.15524/) (2024)

## Built on by (most cited first, 1 of 1)

- [Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks](https://mlsanity.com/paper/2509.10077/) (2025)

## Abstract (arXiv)

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.