Local to global normalization dynamic by nonlinear local interactions

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Here, I present a novel method for normalizing a finite set of numbers, which is studied by the domain of biological vision. Normalizing in this context means searching the maximum and minimum number in a set and then rescaling all numbers such that they fit into a numerical interval. My method computes the minimum and maximum number by two pseudo-diffusion processes in separate diffusion layers. Activity of these layers feed into a third layer for performing the rescaling operation. The dynamic of the network is richer than merely performing a rescaling of its input, and reveals phenomena like contrast detection, contrast enhancement and a transient compression of the numerical range of the input. Apart from presenting computer simulations, some properties of the diffusion operators and the network are analysed mathematically. Furthermore, a method is proposed for to freeze the model's state when adaptation is observed. © 2007 Elsevier Ltd. All rights reserved.




Keil, M. S. (2008). Local to global normalization dynamic by nonlinear local interactions. Physica D: Nonlinear Phenomena, 237(6), 732–744. https://doi.org/10.1016/j.physd.2007.10.011

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