Local to global normalization dynamic by nonlinear local interactions |
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Authors: | Matthias S Keil |
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Institution: | Basic Psychology Department, Faculty for Psychology, University of Barcelona (UB), Passeig de la Vall d’Hebron 171, E-08035 Barcelona, Spain |
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Abstract: | 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. |
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Keywords: | Adaptation Normalization Diffusion Network |
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