K$_{T}$ Attractor Lengths 1 2 3 4 1 2 5 10 20 50 100 200 500 K=L K=1 L=1 K=L K=1 L=1 K=L K=1 L=1 K=L K=1 L=1 Figure 7: Attractor Lengths for cases 1, 2 & 3 ( K = L , K = 1, and L = 1, respectively). Notice logarithmic scale. than a balanced MRBN (case 1), which have a higher Le than extreme MRBNs with a minimal µ = 1 N$_{T}$ (case 5). This can be explained by the fact that in practice attractor lengths grow algebraically with N (for deterministic updating schemes, as the one used in this paper) [17]. For the same value of N$_{T}$ , higher values of µ imply a higher N per module. Having more nodes per module allows the possibility of more combinations of states in an attractor, increasing its length. It is not so much that a large N favors longer attractors, but a small N restricts their possibility. For K ≤ 2, classical RBNs have the lowest Le . Here the modular cases can have longer attractors because of the combination of modular attractors is possible with a low L , as explained for A . 15