# Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks

arXiv: https://arxiv.org/abs/2407.05739 · HTML: https://mlsanity.com/paper/2407.05739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [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)
- [Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks](https://mlsanity.com/paper/2303.04347/) (2023)
- [Attention Spiking Neural Networks](https://mlsanity.com/paper/2209.13929/) (2022)
- [Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks](https://mlsanity.com/paper/2002.10085/) (2020)
- [Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks](https://mlsanity.com/paper/2312.06372/) (2023)
- [BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons](https://mlsanity.com/paper/2105.13004/) (2021)
- [Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks](https://mlsanity.com/paper/2210.06386/) (2022)

## Built on by (most cited first, 3 of 3)

- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Towards Efficient and Accurate Spiking Neural Networks via Adaptive Bit Allocation](https://mlsanity.com/paper/2506.23717/) (2025)
- [All in one timestep: Enhancing Sparsity and Energy efficiency in Multi-level Spiking Neural Networks](https://mlsanity.com/paper/2510.24637/) (2025)

## Abstract (arXiv)

Since proposed, spiking neural networks (SNNs) gain recognition for their high performance, low power consumption and enhanced biological interpretability. However, while bringing these advantages, the binary nature of spikes also leads to considerable information loss in SNNs, ultimately causing performance degradation. We claim that the limited expressiveness of current binary spikes, resulting in substantial information loss, is the fundamental issue behind these challenges. To alleviate this, our research introduces a multi-bit information transmission mechanism for SNNs. This mechanism expands the output of spiking neurons from the original single bit to multiple bits, enhancing the expressiveness of the spikes and reducing information loss during the forward process, while still maintaining the low energy consumption advantage of SNNs. For SNNs, this represents a new paradigm of information transmission. Moreover, to further utilize the limited spikes, we extract effective signals from the previous layer to re-stimulate the neurons, thus encouraging full spikes emission across various bit levels. We conducted extensive experiments with our proposed method using both direct training method and ANN-SNN conversion method, and the results show consistent performance improvements.