# Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods

arXiv: https://arxiv.org/abs/2405.04289 · HTML: https://mlsanity.com/paper/2405.04289/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2024.

## Built on by (most cited first, 25 of 25)

- [Quantized Spike-driven Transformer](https://mlsanity.com/paper/2501.13492/) (2025)
- [Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients](https://mlsanity.com/paper/2503.03272/) (2025)
- [SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) Complexity](https://mlsanity.com/paper/2505.10352/) (2025)
- [SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition](https://mlsanity.com/paper/2406.15034/) (2024)
- [Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning](https://mlsanity.com/paper/2412.09379/) (2024)
- [Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space](https://mlsanity.com/paper/2504.15371/) (2025)
- [Learning from Dense Events: Towards Fast Spiking Neural Networks Training via Event Dataset Distillation](https://mlsanity.com/paper/2511.12095/) (2025)
- [Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression](https://mlsanity.com/paper/2407.08744/) (2024)
- [Topology Optimization of Random Memristors for Input-Aware Dynamic SNN](https://mlsanity.com/paper/2407.18625/) (2024)
- [Spiking Neural Network: a low power solution for physical layer authentication](https://mlsanity.com/paper/2505.12647/) (2025)
- [Spiking Neural Networks for SAR Interferometric Phase Unwrapping: A Theoretical Framework for Energy-Efficient Processing](https://mlsanity.com/paper/2506.20782/) (2025)
- [SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning](https://mlsanity.com/paper/2507.02945/) (2025)
- [Spiking Vocos: An Energy-Efficient Neural Vocoder](https://mlsanity.com/paper/2509.13049/) (2025)
- [Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient](https://mlsanity.com/paper/2511.08708/) (2025)
- [Toward Real-Time Surgical Scene Segmentation via a Spike-Driven Video Transformer with Spike-Informed Pretraining](https://mlsanity.com/paper/2512.21284/) (2025)
- [Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture](https://mlsanity.com/paper/2601.00806/) (2025)
- [SpikySpace: A Spiking State Space Model for Energy-Efficient Time Series Forecasting](https://mlsanity.com/paper/2601.02411/) (2026)
- [SpikingGamma: Surrogate-Gradient Free and Temporally Precise Online Training of Spiking Neural Networks with Smoothed Delays](https://mlsanity.com/paper/2602.01978/) (2026)
- [SpikeCLR: Contrastive Self-Supervised Learning for Few-Shot Event-Based Vision using Spiking Neural Networks](https://mlsanity.com/paper/2603.16338/) (2026)
- [Reconstructing Spiking Neural Networks Using a Single Neuron with Autapses](https://mlsanity.com/paper/2603.24692/) (2026)
- [NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Framework](https://mlsanity.com/paper/2605.15058/) (2026)
- [Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects](https://mlsanity.com/paper/2605.27407/) (2026)
- [Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization](https://mlsanity.com/paper/2606.01945/) (2026)
- [Burst Spiking Neural Networks](https://mlsanity.com/paper/2607.11914/) (2026)
- [Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability](https://mlsanity.com/paper/2608.11506/) (2026)

## Abstract (arXiv)

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends. The reviewed papers are collected at https://github.com/zhouchenlin2096/Awesome-Spiking-Neural-Networks