Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.
Complex topological features of reservoirs shape learning performances in bio-inspired recurrent neural networks
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Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos
Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.