Solving 2-D Pattern Matching using Networks of Polarized Picture Processors with Circular Permutation

We define a new bio-inspired computational model for deciding 2-dimensional languages that is directly derived from the one in [13]. The described model has a slightly different accepting protocol as the previous one and, besides the deletion and substitution operations, we now consider a new type of operation, namely circular permutation. We show that by using circular permutations, such networks can recognize any input that contains a given sub-picture as where our previous model limited one of the pattern dimensions to a size of at most 3.