将词向量转为脉冲信号，实现低功耗脑启发记忆系统
Word2Spike: Poisson Rate Coding for Associative Memories and Neuromorphic Algorithms
- 用泊松过程将词向量映射为脉冲序列，构建神经形态吸引子状态
- 在1万词上实现97%语义相似度与100%重建准确率
- 抗噪能力强，适合部署于类脑计算硬件的联想记忆任务
脉冲神经网络为实现节能、类脑联想记忆提供了可能。本文提出Word2Spike，一种结合连续词向量与神经形态架构的新速率编码机制。通过泊松过程建立多维词向量到脉冲基吸引子状态的一一映射。采用BitNet b1.58量化，在SimLex-999数据集上保持97%的语义相似度，同时在OpenAI text-embedding-3-large的10,000个词上实现100%重建准确率。即使引入噪声，类比推理性能仍保持原始嵌入的100%，表明该机制在神经形态系统中具备稳健的语义编码能力。下一步将把该映射集成至脉冲变换器和液体状态机（类霍普菲尔德网络）中进行评估。
原文摘要 · Abstract (English)
Spiking neural networks offer a promising path toward energy-efficient, brain-like associative memory. This paper introduces Word2Spike, a novel rate coding mechanism that combines continuous word embeddings and neuromorphic architectures. We develop a one-to-one mapping that converts multi-dimensional word vectors into spike-based attractor states using Poisson processes. Using BitNet b1.58 quantization, we maintain 97% semantic similarity of continuous embeddings on SimLex-999 while achieving 100% reconstruction accuracy on 10,000 words from OpenAI's text-embedding-3-large. We preserve analogy performance (100% of original embedding performance) even under intentionally introduced noise, indicating a resilient mechanism for semantic encoding in neuromorphic systems. Next steps include integrating the mapping with spiking transformers and liquid state machines (resembling Hopfield Networks) for further evaluation.
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