用脑启发的网格编码压缩时空序列，支持长期预测与规划
Vector Quantization in the Brain: Grid-like Codes in World Models
- 基于吸引子网络生成动态码本，实现时空联合压缩
- 在多种任务中实现高效编码与下游性能提升
- 适合研究神经编码机制或构建高效世界模型者
我们提出一种脑启发的网格编码量化方法（GCQ），通过吸引子动力学中的网格样模式，将观察-动作序列压缩为离散表示。与传统静态输入的向量量化不同，GCQ采用动作条件化的码本，其中码字来自连续吸引子神经网络，并根据动作动态选择。该方法实现了空间与时间的联合压缩，构成统一的世界模型，支持长时序预测、目标导向规划和逆向建模。在多样任务上的实验表明，GCQ在紧凑编码和下游任务表现上均有效。本工作既提供了一种高效的序列建模工具，也为神经系统中网格编码的形成提供了理论视角。
原文摘要 · Abstract (English)
We propose Grid-like Code Quantization (GCQ), a brain-inspired method for compressing observation-action sequences into discrete representations using grid-like patterns in attractor dynamics. Unlike conventional vector quantization approaches that operate on static inputs, GCQ performs spatiotemporal compression through an action-conditioned codebook, where codewords are derived from continuous attractor neural networks and dynamically selected based on actions. This enables GCQ to jointly compress space and time, serving as a unified world model. The resulting representation supports long-horizon prediction, goal-directed planning, and inverse modeling. Experiments across diverse tasks demonstrate GCQ's effectiveness in compact encoding and downstream performance. Our work offers both a computational tool for efficient sequence modeling and a theoretical perspective on the formation of grid-like codes in neural systems.
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