English(EN) NeuronSpark: Selective Spiking State Space Models with Adaptive Computation for Language Modeling

NeuronSpark：揭示具有自适应计算的脉冲语言模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了NeuronSpark，这是一种利用稀疏脉冲神经元实现自适应计算的新型语言模型。与应用统一放电敏感性的传统模型不同，NeuronSpark的神经元根据输入令牌内容调整其衰减率、增益和放电阈值。这种方法允许从随机初始化进行高效、大规模的预训练，而无需依赖现有模型骨干。NeuronSpark在零样本任务上表现出竞争力，并且与Transformer和Mamba模型相比，尤其是在FPGA等硬件上，显著降低了能耗和计算资源。 AI

影响引入了一种新颖、节能的语言建模方法，使用脉冲神经网络，可能影响未来的硬件加速人工智能。

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arXiv cs.AI TIER_1 English(EN) · Zhengzheng Tang · 2026-10-12 04:00

NeuronSpark：用于语言建模的选择性脉冲状态空间模型与自适应计算

arXiv:2603.16148v2 Announce Type: replace Abstract: Spiking neural networks promise brain-inspired neural dynamics, yet native large-scale spiking language-model pretraining remains largely unexplored relative to conversion, distillation, and backbone-adaptation approaches. We di…

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NeuronSpark：用于语言建模的选择性脉冲状态空间模型与自适应计算

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