Self stabilising (Homeostatic) minimal path tessellation. Uses the principles of the attract/repel algorithm described by Paul Coats in his book Programming.Architecture to emerge a voronoi structure rather than the traditional computational approach. The complexity of the emergent forms can be much higher then defining them in pure geometric ways.
- Use CONTROLS to change parameters:
- Left mouse click to add anchor point
- Right mouse click to add particles
Cellular automaton rules as presented in the August 1988 issue of Scientific American by Professor A. K. Dewdney
This cellular automaton provides an illustration of order emerging "spontaneously" from chaos. Starting from an array whose cells are in randomly assigned states, patterns eventually emerge. In other words, non-randomness emerges from randomness. This has significance for the question of whether complicated biological organisms can emerge from simpler ones as a result of chance occurrences (such as genetic mutations).
This sketch represents a 2D self organising feature map. The SOM is presented with a number of bezier splines to cognize/learn. These inputs can be seen along the bottom. The grey circles represent the learn radius.
To control the following parameter click in the applet window and;
Press q / w to toggle learn radius.
Press a / s to toggle learn rate.
press z / x to toggle time.
This sketch represents a self organising feature map embedded in 3D Cartesian space coupled with a system of agents [points]. The agents are indicated by the small black points. The green points indicate the position of the BMU`s [winners] within the SOM. The black all encompassing box represents the extent of the universe in which the systems are embedded in.
Press q / w to toggle learn radius.
Press a / s to toggle learn rate.
press z / x to toggle time.