Scale-Dependence of Spatiotemporal Filters Inspired by Cellular Automata

We discuss analysis tools of spatiotemporal patterns. These tools are based on nearest-neighbor considerations similar to cellular automata. Application of these methods to a spatiotemporal data set means selecting certain scales in space and in time. Focusing on spatial length we show that the dependence of the results on this scale can be used to quantify separately the contribution to the dynamics of measurement noise and of dynamical (internal) noise, respectively. In particular, we test the spatiotemporal filters using sample data generated with a network of coupled Sel’kov oscillators. Possible application of our results to biological systems are briefly discussed.