ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization
Spiking neural networks (SNNs) are promising for edge sensing due to their event-driven computation and temporal filtering capability. However, standard leaky integrate-and-fire (LIF) neurons communicate only through binary spikes, which severely limit representational capacity. Existing multi-level spiking neurons improve information transmission, but often rely on uniform quantization that mismatches membrane-potential distributions or introduces costly synaptic multiplications. In this paper, we propose ShiftLIF, a multi-level spiking neuron that maps membrane potentials to a logarithmically spaced power-of-two spike set. This design provides finer representation in the small-amplitude...
| Authors | Kaiwen Tang, Di Yu, Jiaqi Zheng et al. |
|---|---|
| Primary category | cs.NE |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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