A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.
Ikemoto, Noise-modulated neural networks for selectively functional- izing sub-networks by exploiting stochastic resonance, Neurocomputing 448 (2021) 1–9
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Spatial Partial Functionalization of Neural Networks based on Noise Fields
A crossing activation function combined with virtual noise fields allows one neural network to learn multiple functions assigned to different noise locations, with capacity rising when noise arrangement matches function proximity.