Organization of computation in reservoir computing

Abstract:Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations. Although reservoir performance is often characterized through memory, nonlinearity, and their tradeoff, such aggregate measures do not reveal how task-relevant information is organized within the state space. Here, we introduce an eigen-spectral decomposition framework linking the degree-wise information processing capacity to the corresponding state space modes. As a result, we are able to quantify the degree-wise representation energy, and show that in some cases, substantial amounts of information processing capacity may reside in low-energy modes that are vulnerable to experimental noise. These results suggest that useful reservoir computation depends not only on dimensionality expansion, but also on the geometric organization of task-relevant information, with direct implications on physical reservoir computers.

Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)

arXiv:2607.17858 [cs.NE]

(or arXiv:2607.17858v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2607.17858 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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