Moulin Jean-Paul
Département d'information et de recherche médicale, Centre hospitalier interdépartemental de Clermont-de-l'Oise, 2, rue des Finets, 60607 Clermont-de-l'Oise cedex, France.
C R Biol. 2006 Mar;329(3):137-47. doi: 10.1016/j.crvi.2005.12.002. Epub 2006 Jan 18.
Living systems are capable to have appropriate responses to unpredictable environment. This kind of self-organization seems to operate as a self-programming machine, i.e. an organization able to modify itself. Until now the models of self-organization of living beings proposed are functions solutions of differential systems or transition functions of automata. These functions are fixed and these models are therefore unable to modify their organization. On the other hand, computer science propose a lot of models having the properties of adaptive systems of living beings, but all these models depend on the comparison between a goal and the results and ingenious choices of parameters by programmers, whereas there are no programmer's intention nor choice in the living systems. From two best known examples of adaptive systems of living beings, nervous system and immune system that have in common that the external signals modify the rewriting of their organization and therefore work as self-organizing machines, we devised machines with a finite set of inputs, based upon a recurrence, are able to rewrite their organization (Self-programming machines or m(sp)) whenever external conditions vary and have striking properties of adaptation. M(sp) have similar properties whatever the operation defining the recurrence maybe. These results bring us to make the following statement: adaptive properties of living systems can be explained by their ability to rewrite their organization whenever external conditions vary under the only assumption that the rewriting mechanism be a deterministic constant recurrence in a finite state set.
生命系统能够对不可预测的环境做出适当反应。这种自组织似乎像一台自我编程的机器一样运作，即一个能够自我修改的组织。到目前为止，所提出的生物自组织模型是微分系统的函数解或自动机的转移函数。这些函数是固定的，因此这些模型无法修改它们的组织。另一方面，计算机科学提出了许多具有生物自适应系统特性的模型，但所有这些模型都依赖于目标与结果之间的比较以及程序员对参数的巧妙选择，而在生命系统中不存在程序员的意图或选择。从生物自适应系统的两个最著名例子——神经系统和免疫系统来看，它们的共同之处在于外部信号会修改其组织的重写，因此像自组织机器一样运作。我们设计了一种具有有限输入集的机器，基于递归，每当外部条件变化时，它能够重写其组织（自我编程机器或m(sp)），并具有显著的适应特性。无论定义递归的操作是什么，m(sp)都具有相似的特性。这些结果使我们得出以下结论：只要外部条件在唯一假设即重写机制是有限状态集中的确定性常数递归的情况下发生变化，生命系统的自适应特性就可以通过它们重写自身组织的能力来解释。
