Noise-driven manifestation of learning in mature neural networks.
We show that the generalization capability of a mature thresholding neural network to process above-threshold disturbances in a noise-free environment is extended to subthreshold disturbances by ambient noise without retraining. The ability to benefit from noise is intrinsic and does not have to be learned separately. Nonlinear dependence of sensitivity with noise strength is significantly narrower than in individual threshold systems. Noise has a minimal effect on network performance for above-threshold signals. We resolve two seemingly contradictory responses of trained networks to noise-their ability to benefit from its presence and their robustness against noisy strong disturbances.
Monterola
Christopher
C
National Institute of Physics, University of the Philippines, Diliman, Quezon City 1101, The Philippines.
Saloma
Caesar
C
D000818:Animals; D006801:Humans; D007858:Learning; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D009622:Noise; D012684:Sensory Thresholds / Q000502:physiology