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记忆与熵

Memory and Entropy.

作者信息

Rovelli Carlo

机构信息

Aix Marseille University, Université de Toulon, CNRS, CPT, 13288 Marseille, France.

Perimeter Institute, 31 Caroline Street North, Waterloo, ON N2L 2Y5, Canada.

出版信息

Entropy (Basel). 2022 Jul 24;24(8):1022. doi: 10.3390/e24081022.

Abstract

I study the physical nature of traces. Surprisingly, (i) systems separation with (ii) temperature differences and (iii) long thermalization times are sufficient conditions to produce macroscopic traces. Traces of the past are ubiquitous because these conditions are largely satisfied in our universe. I quantify these thermodynamical conditions for memory and derive an expression for the maximum amount of information stored in such memories as a function of the relevant thermodynamical parameters. This mechanism transforms low entropy into available information. I suggest that all macroscopic information has this origin in past low entropy.

摘要

我研究痕迹的物理本质。令人惊讶的是,(i)系统分离、(ii)温度差异以及(iii)较长的热化时间是产生宏观痕迹的充分条件。过去的痕迹无处不在,因为这些条件在我们的宇宙中大多是满足的。我对这些记忆的热力学条件进行了量化,并推导出了作为相关热力学参数函数的此类记忆中存储的最大信息量的表达式。这种机制将低熵转化为可用信息。我认为所有宏观信息都源于过去的低熵。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6d58/9394364/d18fea68dbd3/entropy-24-01022-g001.jpg

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