Many systems in biology and other sciences employ collaborative, collective communication strategies for improved efficiency and adaptive benefit. One such paradigm of particular interest is the community estimation of a dynamic signal, when, for example, an epithelial tissue of cells must decide whether to react to a given dynamic external concentration of stress-signaling molecules. At the level of dynamic cellular communication, however, it remains unknown what effect, if any, arises from communication beyond the mean field level. What are the limits and benefits to communication across a network of neighbor interactions? What is the role of Poissonian versus super-Poissonian dynamics in such a setting? How does the particular topology of connections impact the collective estimation and that of the individual participating cells? In this article we construct a robust and general framework of signal estimation over continuous-time Markov chains in order to address and answer these questions. Our results show that in the case of Possonian estimators, the communication solely enhances convergence speed of the mean squared error (MSE) of the estimators to their steady-state values while leaving these values unchanged. However, in the super-Poissonian regime, the MSE of estimators significantly decreases by increasing the number of neighbors. Surprisingly, in this case, the clustering coefficient of an estimator does not enhance its MSE while still reducing the total MSE of the population.

中文翻译：

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gab的礼物：探究通讯细胞网络中动态集中感测的极限

生物学和其他科学领域的许多系统都采用协作，集体的交流策略来提高效率和适应性收益。其中一种特别令人感兴趣的范例是动态信号的社区估计，例如，当细胞的上皮组织必须决定是否对应力信号分子的给定动态外部浓度做出反应时，就对动态信号进行社区评估。然而，在动态蜂窝通信的水平上，尚不清楚通信超出平均场水平会产生什么影响，如果有的话。通过邻居交互网络进行通信的局限性和收益是什么？在这种情况下，泊松与超泊松动力学的作用是什么？连接的特定拓扑如何影响集体估计和各个参与单元的估计？在本文中，我们构建了一个健壮且通用的连续时间马尔可夫链信号估计框架，以解决并回答这些问题。我们的结果表明，在Possonian估计器的情况下，通信仅提高了估计器的均方误差（MSE）到其稳态值的收敛速度，而这些值保持不变。但是，在超级泊松体系中，估计数的MSE通过增加邻居数而显着降低。令人惊讶的是，在这种情况下，