There is evidence that biological systems, such as the brain, work at a critical regime robust to noise, and are therefore able to remain in it under perturbations. In this work, we address the question of robustness of critical systems to noise. In particular, we investigate the robustness of stochastic cellular automata (CAs) at criticality. A stochastic CA is one of the simplest stochastic models showing criticality. The transition state of stochastic CA is defined through a set of probabilities. We systematically perturb the probabilities of an optimal stochastic CA known to produce critical behavior, and we report that such a CA is able to remain in a critical regime up to a certain degree of noise. We present the results using error metrics of the resulting power-law fitting, such as Kolmogorov-Smirnov statistic and Kullback-Leibler divergence. We discuss the implication of our results in regards to future realization of brain-inspired artificial intelligence systems.
翻译:有证据表明,像大脑这样的生物系统在对噪音具有活力的关键系统中工作,因此能够在扰动下继续工作。在这项工作中,我们处理关键系统对噪音的稳健性问题。特别是,我们调查了细胞自动自动成像(CAs)在临界性方面的稳健性。随机性CA是显示临界性的最简单的随机性模型之一。随机性CA的过渡状态是通过一系列概率来界定的。我们系统地破坏已知产生关键行为的最佳随机性CA的概率,我们报告说,这种CA能够在一个关键系统中保持某种程度的噪音。我们用错误的指数来说明由此产生的电法安装结果,例如科尔莫戈夫-斯米尔诺夫统计和库尔韦-利韦尔的差异。我们讨论了我们的结果对未来实现大脑激励型人工智能系统的影响。