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The efficacy of a perceptron is modeled using a rectified adaptive nonlinear unit (RANU). For the k-th mode, the RANU is given by
where the quality factors and govern the strength of rectification, and the functions and quantify relevance. A mode is more relevant as deviates farther from the bifurcation reference, . Relevance is modeled as a function of x, where . A linear rectifier is recovered when . The nonlinear functions used in SPLOC-RNN are shown in Fig. Fig.22g.
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