AI RESEARCH
QB-LIF: Learnable-Scale Quantized Burst Neurons for Efficient SNNs
arXiv CS.CV
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ArXi:2604.25688v1 Announce Type: new Binary spike coding enables sparse and event-driven computation in spiking neural networks (SNNs), yet its 1-bit-per-timestep representation fundamentally limits information throughput. This bottleneck becomes increasingly restrictive in deep architectures under short simulation horizons. We propose the Quantized Burst-LIF (QB-LIF) neuron, which reformulates burst spiking as a saturated uniform quantization of membrane potentials with a learnable scale.