Unsupervised Particle Tracking with Neuromorphic Computing

Unsupervised Particle Tracking with Neuromorphic Computing

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 part...

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Bibliographic Details
Main Authors: Emanuele Coradin, Fabio Cufino, Muhammad Awais, Tommaso Dorigo, Enrico Lupi, Eleonora Porcu, Jinu Raj, Fredrik Sandin, Mia Tosi
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Particles
Subjects:
particle detectors
particle tracking
neuromorphic computing
unsupervised learning
spiking neural networks
genetic algorithms
Online Access:https://www.mdpi.com/2571-712X/8/2/40
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