Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing

Abstract: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.

Chaotic Dynamics (nlin.CD); Machine Learning (cs.LG)

arXiv:2609.01914 [nlin.CD]

(or arXiv:2609.01914v1 [nlin.CD] for this version)

https://doi.org/10.48550/arXiv.2609.01914 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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