Abstract
Spectral analysis of neural networks with stochastic weights (stemming from the solution of Schrödinger’s diffusion equation) has shown that: (a) The Gaussian basis functions of the weights express the distribution of the energy with respect to the weights’ value. The smaller the spread of the basis functions is, the larger becomes the spectral (energy) content that can be captured therein. Narrow spread of the basis functions results in wide range of frequencies of the Fourier transformed pulse, (b) The stochastic weights satisfy an equation which is analogous to the principle of uncertainty.
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Rigatos, G.G. (2015). Spectral Analysis of Neural Models with Stochastic Weights. In: Advanced Models of Neural Networks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43764-3_11
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DOI: https://doi.org/10.1007/978-3-662-43764-3_11
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