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We address the important theoretical question why a recurrent neural networkwith fixed weights can adaptively classify time-varied signals in the presenceof additive noise and parametric perturbations. We provide a mathematical proofassuming that unknown parameters are allowed to enter the signal nonlinearlyand the noise amplitude is sufficiently small.

Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof