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Research questionWhen do explicitly organized recurrent interactions improve dynamical learning over generic reservoirs under matched state dimensions and controlled tuning?Dynamical learners can organize state interactions explicitly or leave them to a generic recurrent parameterization. Their relative value may depend on the task, even when state dimension, readout, and tuning procedures are controlled.
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Latest papersRecent research connected to this question, newest first.Explicit Interaction Architectures for Dynamical Learning: A Controlled Study of Structural Inductive BiasThe evidence concerns one- and two-layer structured causal recurrent units versus a generic echo-state network, all with 12 recurrent states and a linear ridge readout. Hyperparameters are selected by matched random search on disjoint calibration data and evaluated on a custom nonlinear identification task and NARMA10; the results do not establish universal superiority or impose scattering, passivity, or energy-balance constraints.research paper · Sep 3, 2026
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