NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework

2026-05-14

AI Summary

This work addresses the long-standing challenges in spiking neural network (SNN) training research—namely, the lack of a systematic taxonomy and unified evaluation protocols, which have led to poor reproducibility and fragmented progress. To this end, we propose the first fine-grained, systematic classification framework for SNN training algorithms and introduce NeuroTrain, an open-source benchmark built upon snnTorch. NeuroTrain enables, for the first time, modular integration and fair comparison across diverse algorithmic paradigms, including surrogate gradient backpropagation, local and three-factor learning rules, biologically plausible plasticity mechanisms, ANN-to-SNN conversion methods, and unconventional optimization strategies. The framework standardizes the implementation of representative algorithms and supports consistent evaluation across datasets, architectures, and training configurations, thereby significantly enhancing the reproducibility and systematic investigation of SNN training methodologies.

Abstract

The rapid expansion of spiking neural networks (SNNs) has led to a proliferation of training algorithms that differ widely in biological inspiration, computational structure, and hardware suitability. Despite this progress, the field lacks a unified, fine-grained taxonomy that systematically organizes these approaches and clarifies their conceptual relationships. This survey provides a comprehensive taxonomy of SNN training algorithms, spanning surrogate-gradient backpropagation, local and three-factor learning rules, biologically inspired plasticity mechanisms, ANN-to-SNN conversion pipelines, and non-standard optimization strategies. We analyze each class in terms of its computational principles, learning signals, and locality properties. To support reproducible research, we release NeuroTrain, an open-source snnTorch-based framework that implements a representative set of these algorithms within a unified, modular, and extendable framework, enabling consistent benchmarking across datasets, architectures, and training regimes. By consolidating fragmented literature and providing a reusable benchmarking framework, this survey identifies common patterns, highlights open challenges, and outlines promising directions for future work on scalable, efficient SNN training.

Problem

• spiking neural networks
• training algorithms
• taxonomy
• benchmarking
• local learning rules

Innovation

• spiking neural networks
• local learning rules
• benchmarking framework
• taxonomy
• NeuroTrain