Rethinking Reservoir Pruning: A Dynamical Perspective for Echo State Networks

• URL: http://arxiv.org/abs/2608.04593v1
• Date: Wed, 05 Aug 2026 08:56:17 GMT
• Title: Rethinking Reservoir Pruning: A Dynamical Perspective for Echo State Networks
• Abstract summary: We propose Dynamical Mode Pruning (DMP), a reservoir pruning method that ranks neurons by their contribution to dominant transition modes.<n>DMP improves or preserves forecasting accuracy while reducing redundant reservoir components.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Echo State Networks (ESNs) offer an efficient framework for temporal prediction, but their randomly initialized reservoirs are often over-parameterized and dynamically redundant. Existing pruning methods largely rely on static connectivity or activation statistics, which may overlook neurons that shape input-driven state transitions. We propose Dynamical Mode Pruning (DMP), a reservoir pruning method that ranks neurons by their contribution to dominant transition modes obtained from a trajectory-averaged Jacobian Gramian. DMP removes low-impact units and retrains only the readout. Experiments on chaotic and real-world time-series benchmarks show that DMP improves or preserves forecasting accuracy while reducing redundant reservoir components. Our results suggest that dynamical influence is a useful criterion for reservoir refinement beyond static structural importance alone.

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