# Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks

arXiv: https://arxiv.org/abs/2312.06372 · HTML: https://mlsanity.com/paper/2312.06372/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 49 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Deep Residual Learning in Spiking Neural Networks](https://mlsanity.com/paper/2102.04159/) (2021)
- [Model compression via distillation and quantization](https://mlsanity.com/paper/1802.05668/) (2018)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks](https://mlsanity.com/paper/1908.05033/) (2019)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks](https://mlsanity.com/paper/1909.13144/) (2019)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation](https://mlsanity.com/paper/2005.01807/) (2020)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (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)
- [DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks](https://mlsanity.com/paper/2008.03658/) (2020)
- [Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2304.05627/) (2023)
- [Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State](https://mlsanity.com/paper/2109.14247/) (2021)
- [Reducing ANN-SNN Conversion Error through Residual Membrane Potential](https://mlsanity.com/paper/2302.02091/) (2023)
- [Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes](https://mlsanity.com/paper/2302.10685/) (2023)
- [Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization](https://mlsanity.com/paper/2305.02099/) (2023)
- [Spiking PointNet: Spiking Neural Networks for Point Clouds](https://mlsanity.com/paper/2310.06232/) (2023)
- [Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes](https://mlsanity.com/paper/2204.13271/) (2022)

## Built on by (most cited first, 30 of 38)

- [SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms](https://mlsanity.com/paper/2406.03287/) (2024)
- [Learning A Spiking Neural Network for Efficient Image Deraining](https://mlsanity.com/paper/2405.06277/) (2024)
- [Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks](https://mlsanity.com/paper/2401.04486/) (2024)
- [Quantized Spike-driven Transformer](https://mlsanity.com/paper/2501.13492/) (2025)
- [Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer](https://mlsanity.com/paper/2503.00226/) (2025)
- [Rethinking Spiking Neural Networks from an Ensemble Learning Perspective](https://mlsanity.com/paper/2502.14218/) (2025)
- [Ternary Spike-based Neuromorphic Signal Processing System](https://mlsanity.com/paper/2407.05310/) (2024)
- [FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion](https://mlsanity.com/paper/2403.18388/) (2024)
- [State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons](https://mlsanity.com/paper/2504.02591/) (2025)
- [Cross Knowledge Distillation between Artificial and Spiking Neural Networks](https://mlsanity.com/paper/2507.09269/) (2025)
- [Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks](https://mlsanity.com/paper/2407.05739/) (2024)
- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Binary Event-Driven Spiking Transformer](https://mlsanity.com/paper/2501.05904/) (2025)
- [Improving Performance of Spike-based Deep Q-Learning using Ternary Neurons](https://mlsanity.com/paper/2506.03392/) (2025)
- [ReverB-SNN: Reversing Bit of the Weight and Activation for Spiking Neural Networks](https://mlsanity.com/paper/2506.07720/) (2025)
- [SpikingMamba: Towards Energy-Efficient Large Language Models via Knowledge Distillation from Mamba](https://mlsanity.com/paper/2510.04595/) (2025)
- [Temporal Reversal Regularization for Spiking Neural Networks: Hybrid Spatio-Temporal Invariance for Generalization](https://mlsanity.com/paper/2408.09108/) (2024)
- [Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation](https://mlsanity.com/paper/2506.23717/) (2025)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [DarkShake-DVS: Event-based Human Action Recognition under Low-light andShaking Camera Conditions](https://mlsanity.com/paper/2605.20680/) (2026)
- [SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams](https://mlsanity.com/paper/2503.20315/) (2025)
- [BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning](https://mlsanity.com/paper/2604.23165/) (2026)
- [Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models](https://mlsanity.com/paper/2604.25570/) (2026)
- [Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients](https://mlsanity.com/paper/2605.27412/) (2026)
- [Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons](https://mlsanity.com/paper/2502.14487/) (2025)
- [DTA: Dual Temporal-channel-wise Attention for Spiking Neural Networks](https://mlsanity.com/paper/2503.10052/) (2025)
- [Integer Binary-Range Alignment Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2506.05679/) (2025)
- [Quantization Meets Spikes: Nearly Lossless Conversion at the First Timestep via Polarity Multi-Spike Mapping](https://mlsanity.com/paper/2508.14520/) (2025)
- [Incorporating the Refractory Period into Spiking Neural Networks through Spike-Triggered Threshold Dynamics](https://mlsanity.com/paper/2509.17769/) (2025)
- [Discretized Quadratic Integrate-and-Fire Neuron Model for Deep Spiking Neural Networks](https://mlsanity.com/paper/2510.05168/) (2025)

## Abstract (arXiv)

The Spiking Neural Network (SNN), as one of the biologically inspired neural network infrastructures, has drawn increasing attention recently. It adopts binary spike activations to transmit information, thus the multiplications of activations and weights can be substituted by additions, which brings high energy efficiency. However, in the paper, we theoretically and experimentally prove that the binary spike activation map cannot carry enough information, thus causing information loss and resulting in accuracy decreasing. To handle the problem, we propose a ternary spike neuron to transmit information. The ternary spike neuron can also enjoy the event-driven and multiplication-free operation advantages of the binary spike neuron but will boost the information capacity. Furthermore, we also embed a trainable factor in the ternary spike neuron to learn the suitable spike amplitude, thus our SNN will adopt different spike amplitudes along layers, which can better suit the phenomenon that the membrane potential distributions are different along layers. To retain the efficiency of the vanilla ternary spike, the trainable ternary spike SNN will be converted to a standard one again via a re-parameterization technique in the inference. Extensive experiments with several popular network structures over static and dynamic datasets show that the ternary spike can consistently outperform state-of-the-art methods. Our code is open-sourced at https://github.com/yfguo91/Ternary-Spike.