Assessing the robustness of critical behavior in stochastic cellular automata

There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able remain in it under perturbations. In this work, we address question of robustness systems noise. particular, investigate stochastic cellular automata (CAs) criticality. A CA one simplest models showing The transition state defined through set probabilities. We systematically perturb probabilities an optimal known produce behavior, report up certain degree present results using error metrics resulting power-law fitting, Kolmogorov-Smirnov statistic Kullback-Leibler divergence. discuss implication our regards future realization brain-inspired artificial intelligence systems.