In many complex systems, whether biological or artificial, the thermodynamic costs of communication among their components are large. These systems also tend to split information transmitted between any two components across multiple channels. A common hypothesis is that such inverse multiplexing strategies reduce total thermodynamic costs. So far, however, there have been no physics-based results supporting this hypothesis. This gap existed partially because we have lacked a theoretical framework that addresses the interplay of thermodynamics and information in off-equilibrium systems. Here we present the first study that rigorously combines such a framework, stochastic thermodynamics, with Shannon information theory. We develop a minimal model that captures the fundamental features common to a wide variety of communication systems, and study the relationship between the entropy production of the communication process and the channel capacity, the canonical measure of the communication capability of a channel. In contrast to what is assumed in previous works not based on first principles, we show that the entropy production is not always a convex and monotonically increasing function of the channel capacity. However, those two properties are recovered for sufficiently high channel capacity. These results clarify when and how to split a single communication stream across multiple channels.

翻译：在许多复杂系统中，无论是生物系统还是人工系统，其各组件间通信的热力学成本通常非常高昂。这些系统还倾向于将任意两个组件之间传输的信息拆分为多个信道。一种常见假设认为，这种反向复用策略能够降低总热力学成本。然而，迄今为止尚无基于物理学的理论结果支持这一假设。这一空白部分源于我们缺乏一个能解决非平衡系统中热力学与信息交互问题的理论框架。本文首次将随机热力学这一框架与香农信息论进行严谨结合，提出了一项开创性研究。我们构建了一个能捕捉各类通信系统共同基本特征的极简模型，并研究了通信过程的熵产生与信道容量——这一衡量信道通信能力的典型指标——之间的关系。与先前未基于第一性原理的研究假设相反，我们发现熵产生并非总是信道容量的凸函数且单调递增。然而，在信道容量足够高时，这两个性质会重新显现。这些结果阐明了何时以及如何将单一通信流分割到多个信道中。