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Stochastic Resonance Enables Bpp/Log∗ Complexity And Universal Approximation In Analog Recurrent Neural Networks, Emmett Redd, A. Steven Younger, Tayo Obafemi-Ajayi
Stochastic Resonance Enables Bpp/Log∗ Complexity And Universal Approximation In Analog Recurrent Neural Networks, Emmett Redd, A. Steven Younger, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
Stochastic resonance (SR) is a natural process that without limit increases the precision of signal measurements in biological and physical sciences. Most artificial neural networks (NNs) are implemented on digital computers of fixed precision. A NN accessing universal approximation and a computational complexity class more powerful that of a Turing machine needs analog signals utilizing SR's limitless precision increase. This paper links an analog recurrent (AR) NN theorem, SR, BPP/log∗ (a physically realizable, super-Turing computation class), and universal approximation so NNs following them can be made computationally more powerful. An optical neural network mimicking chaos indicates super-Turing computation has been …