发表机构
Kyoto University; Department of Electrical Engineering, Kyoto University（京都大学; 京都大学电气工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用耦合达芬振子网络的非对称耦合各向异性，提出物理储备池计算中因果信息滤波的机制，可清除异常信息并保障计算完整性，为容错物理智能提供稳健框架。
AI 中文摘要
我们利用非对称耦合各向异性，展示了物理储备池计算（PRC）中用于因果信息滤波的一种物理机制。通过耦合达芬振子网络，我们表明内部耦合的方向性会在有效势中诱导出空间梯度，建立确定性的上游至下游信息流。这种各向异性可选择性放大语义漂移，在全局计算失效前触发作为物理联锁的宏观鞍结分岔。对行波输入下的50节点系统进行时空分析，我们证实局部相变可有效清除异常信息，同时保留其余节点的计算完整性。结果表明，储备池拓扑的内在因果性为自主可靠性和容错物理智能提供了稳健框架。
英文摘要
We demonstrate a physical mechanism for causal information filtering in a physical reservoir computing (PRC) by exploiting asymmetric coupling anisotropy. Using a network of coupled Duffing oscillators, we show that the directionality of internal coupling induces a spatial gradient in the effective potential, establishing a deterministic upstream-to-downstream information flow. This anisotropy allows for the selective amplification of semantic drifts, triggering a macroscopic saddle-node bifurcation as a physical interlock before global computational failure. Through spatiotemporal analysis of a 50-node system under traveling wave inputs, we confirm that local phase transitions effectively purge anomalous information while preserving the computational integrity of the remaining nodes. The results suggest that the intrinsic causality of the reservoir's topology provides a robust framework for autonomous reliability and fault-tolerant physical intelligence.
Comments14 pages, 9 figures
