Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks
Yongjun Xiao, Xianlong Tian, Yongqi Ding, Pei He, Mengmeng Jing, Lin, Zuo

TL;DR
This paper introduces a multi-bit transmission mechanism for spiking neural networks to improve their expressiveness and performance while maintaining low power consumption, addressing the information loss caused by binary spikes.
Contribution
The paper proposes a novel multi-bit information transmission paradigm for SNNs, enhancing expressiveness and reducing information loss compared to traditional binary spike methods.
Findings
Consistent performance improvements in experiments
Effective utilization of multi-bit spikes for better information transmission
Enhanced neuron stimulation from previous layers
Abstract
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…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural Networks and Applications · Neural dynamics and brain function
