# Unsupervised Particle Tracking with Neuromorphic Computing

arXiv: https://arxiv.org/abs/2502.06771 · HTML: https://mlsanity.com/paper/2502.06771/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 7 of 7)

- [Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings](https://mlsanity.com/paper/2306.17670/) (2023)
- [PyGAD: An Intuitive Genetic Algorithm Python Library](https://mlsanity.com/paper/2106.06158/) (2021)
- [Roadmap to Neuromorphic Computing with Emerging Technologies](https://mlsanity.com/paper/2407.02353/) (2024)
- [Memory via Temporal Delays in weightless Spiking Neural Network](https://mlsanity.com/paper/2202.07132/) (2022)
- [Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions](https://mlsanity.com/paper/2210.11190/) (2022)
- [A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons](https://mlsanity.com/paper/2301.09962/) (2023)
- [Bio-plausible Unsupervised Delay Learning for Extracting Temporal Features in Spiking Neural Networks](https://mlsanity.com/paper/2011.09380/) (2020)

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

- [Spiking Neural Network inference on FPGAs with hls4ml](https://mlsanity.com/paper/2606.10008/) (2026)

## Abstract (arXiv)

We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of the Compact Muon Solenoid Phase II detector. We show how a spiking neural network is capable of successfully identifying in a completely unsupervised way the signal left by charged particles in the presence of conspicuous noise from accidental or combinatorial hits. These results open the way to applications of neuromorphic computing to particle tracking, motivating further studies into its potential for real-time, low-power particle tracking in future high-energy physics experiments.