Fig. 4.

Convergence of the distribution of the recovery intervals ${\rho }_{t{r}_{\text{PD}}}\left(t{r}_{\text{PD}}\right)$ yields iterative convergence for periods ${\rho }_{P_{\text{net}}}\left(P_{\text{net}}\right)$ in PD. A: the map ${\rho }_{t{r}_{\text{PD}}}^k\left(t{r}_{\text{PD}}\right)\to {\rho }_{t{r}_{\text{PD}}}^{k+1}\left(t{r}_{\text{PD}}\right)$ was randomly initialized at k = 0 and used to produce the random distribution of network periods shown here. B: after a single iteration (k = 1) the distribution shows a distinct peak. C: at k = 7, the distribution reaches steady state. D: the histogram for k = 8 is indistinguishable from that for k = 7 in C, indicating that the algorithm has converged to a steady-state distribution. Moreover, there is a strong resemblance between the calculated histogram (solid curve) and the actual experimentally observed histogram (dashed curve).