发表机构
Cornell University; Arizona State University（康奈尔大学; 亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究揭示储层计算中记忆盆地的章鱼状结构，提出线索驱动广义同步可绕过不可预测性实现可靠回忆，且该结构在训练后的循环神经网络中也存在。
AI 中文摘要
可靠的吸引子回忆通常需要宽阔的吸引子盆地，但在基于储层计算的联想记忆中，尽管盆地被不可预测的类 riddled 区域主导，时间线索仍能可靠地恢复动力记忆。我们发现记忆盆地呈现“章鱼状”结构：吸引子附近有一个强健的“头部”，还有纤细、交织的“触手”延伸至状态空间。触手区域的初始状态会产生接近零的不确定性指数，使得有限精度下的回忆记忆实际上不可预测。然而，线索驱动的广义同步绕过了这种不可预测性，将系统推向强健的盆地头部。该机制建立了最小线索持续时间、同步率与盆地头部半径之间的定量关系。训练后的循环神经网络表现出类似的几何结构，表明该现象不仅限于储层计算。
英文摘要
Reliable attractor recall conventionally requires broad basins of attraction. However, in reservoir-computing based associative memory, temporal cues reliably recover dynamical memories despite basins dominated by unpredictable, riddled-like regions. We reveal that memory basins exhibit an ``octopus-like'' structure: a robust ``head'' near the attractor and thin, intertwined ``tentacles'' spanning state space. Initial states in tentacular regions yield near-zero uncertainty exponents, making the recalled memory effectively unpredictable at finite precision. Yet, cue-driven generalized synchronization bypasses this unpredictability, driving the system into the robust basin head. This mechanism yields a quantitative relation linking minimum cue duration, synchronization rate, and basin-head radius. Trained recurrent neural networks exhibit similar geometry, suggesting this phenomenon extends beyond reservoir computing.
