A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Hybrid neuromorphic-ANN models outperform standard deep learning on few-shot benchmarks and under occlusion/impulse noise via astrocytic modulation and spiking dynamics.
A system pairing GAN pose frontalization with memristor neuromorphic classifiers reports up to 96% accuracy on non-frontal face datasets.
citing papers explorer
-
NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework
A taxonomy of SNN training algorithms is presented with the release of NeuroTrain, an open benchmarking framework for reproducible comparisons across datasets and architectures.