An advanced version of the dynamic receptor pattern generation model: the flux model

In the recently described simple model of dynamic receptor pattern generation we used a two dimensional hexagonal area of a regular triangular network, formed by a statistically constant distribution of unit electostatic changes in a dynamic equilibrium. A set of 16 trnasition rules was applied to all units simultaneously; the next state of each unit depended only on the previous state of its six nearest neighbours, and the transition of the total pattern into the new one occurred in a single jump. Hence we designated the initial simple model as "jump model". In this paper we described an advanced version of the model, in which simplified rules are applied to one unit after the other in a sequential order, from left to right, starting with the top row of units. In the advanced version the state of a unit depends not only on that of its six nearest neighbours, but also on the state of all units preceding in sequence the one actually considered. This results in flux-like transitions. We therefore designated the advanced version as the "flux model". It is shown that the flux model represents a closer approximation of physical and biological realities than the original jump model.