# A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

arXiv: https://arxiv.org/abs/2206.06129 · HTML: https://mlsanity.com/paper/2206.06129/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 94 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 30 of 30)

- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks](https://mlsanity.com/paper/2002.10085/) (2020)
- [Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation](https://mlsanity.com/paper/2205.00459/) (2022)
- [Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks](https://mlsanity.com/paper/2105.11654/) (2021)
- [Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization](https://mlsanity.com/paper/1911.00822/) (2019)
- [DART: Distribution Aware Retinal Transform for Event-based Cameras](https://mlsanity.com/paper/1710.10800/) (2017)
- [Surrogate gradients for analog neuromorphic computing](https://mlsanity.com/paper/2006.07239/) (2020)
- [A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule](https://mlsanity.com/paper/1812.06574/) (2018)
- [Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks](https://mlsanity.com/paper/1908.06378/) (2019)
- [Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks](https://mlsanity.com/paper/1805.07866/) (2018)
- [Brain-inspired global-local learning incorporated with neuromorphic computing](https://mlsanity.com/paper/2006.03226/) (2020)
- [BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons](https://mlsanity.com/paper/2105.13004/) (2021)
- [First-spike based visual categorization using reward-modulated STDP](https://mlsanity.com/paper/1705.09132/) (2017)
- [Low-activity supervised convolutional spiking neural networks applied to speech commands recognition](https://mlsanity.com/paper/2011.06846/) (2020)
- [Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes](https://mlsanity.com/paper/2005.03231/) (2020)
- [Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks](https://mlsanity.com/paper/2002.06199/) (2020)
- [Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation](https://mlsanity.com/paper/2010.04434/) (2020)
- [Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons](https://mlsanity.com/paper/1911.08261/) (2019)
- [Supervised Learning in Spiking Neural Networks with FORCE Training](https://mlsanity.com/paper/1609.02545/) (2016)
- [Learning physical properties of liquid crystals with deep convolutional neural networks](https://mlsanity.com/paper/2004.01691/) (2020)
- [Rethinking Pretraining as a Bridge from ANNs to SNNs](https://mlsanity.com/paper/2203.01158/) (2022)
- [Optimizing working memory with heterogeneity of recurrent cortical excitation](https://mlsanity.com/paper/1310.3754/) (2013)

## Built on by (most cited first, 9 of 9)

- [Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion](https://mlsanity.com/paper/2405.17903/) (2024)
- [A Spatial-channel-temporal-fused Attention for Spiking Neural Networks](https://mlsanity.com/paper/2209.10837/) (2022)
- [Automotive Object Detection via Learning Sparse Events by Spiking Neurons](https://mlsanity.com/paper/2307.12900/) (2023)
- [Deep Pulse-Coupled Neural Networks](https://mlsanity.com/paper/2401.08649/) (2023)
- [STOP: Spatiotemporal Orthogonal Propagation for Weight-Threshold-Leakage Synergistic Training of Deep Spiking Neural Networks](https://mlsanity.com/paper/2411.11082/) (2024)
- [ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2503.10195/) (2025)
- [Toward Efficient Spiking Transformers: Synapse Pruning Meets Synergistic Learning-Based Compensation](https://mlsanity.com/paper/2508.01992/) (2025)
- [NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration](https://mlsanity.com/paper/2508.21566/) (2025)
- [Hyperdimensional Decoding of Spiking Neural Networks](https://mlsanity.com/paper/2511.08558/) (2025)

## Abstract (arXiv)

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning~(STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.