  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 analyzed mathematically.
Furthermore, a method is proposed for to freeze the model's state when
adaptation is observed.
