Krawitz Peter, Shmulevich Ilya
Institute for Systems Biology, Seattle, Washington 98103, USA.
Phys Rev Lett. 2007 Apr 13;98(15):158701. doi: 10.1103/PhysRevLett.98.158701. Epub 2007 Apr 9.
The information processing capacity of a complex dynamical system is reflected in the partitioning of its state space into disjoint basins of attraction, with state trajectories in each basin flowing towards their corresponding attractor. We introduce a novel network parameter, the basin entropy, as a measure of the complexity of information that such a system is capable of storing. By studying ensembles of random Boolean networks, we find that the basin entropy scales with system size only in critical regimes, suggesting that the informationally optimal partition of the state space is achieved when the system is operating at the critical boundary between the ordered and disordered phases.
复杂动态系统的信息处理能力体现在其状态空间被划分为不相交的吸引子盆地，每个盆地中的状态轨迹流向其相应的吸引子。我们引入了一个新的网络参数——盆地熵，作为衡量此类系统能够存储的信息复杂性的指标。通过研究随机布尔网络的集合，我们发现盆地熵仅在临界状态下随系统大小而缩放，这表明当系统在有序和无序相之间的临界边界运行时，可实现状态空间的信息最优划分。
