看來 Mrphs 不只是會說中文而已，怕是還很精通辭令的吧！特設問道︰方才分明所說之『關係』太過牽強的吧？沒想到 Mrphs 接著又講︰事出必有因，無風不起浪，這『原由』就出在那
《 M♪o 之 TinyIoT 》
一系列文本的啊。從
《 M♪o 之 TinyIoT ︰ 《破題》》始，至
《 M♪o 之 TinyIoT 《起承轉合》之未來鳥瞰！！》終。
既然是寫『踢呦ㄊㄜˋ』， M♪o 焉能沒讀過，據知這『 It 網』即是啟發於『自生自成』系統，
《 M♪o 之 TinyIoT ︰ 《起合》※補充二︰自生自成》───
因此創造了一個『 Autopoiesis 』的新字，也並不代表我們就真的有了一個『自我生成』之『科學理論』。
馬圖拉那與瓦雷拉所描述的『自生自成』機器之所以難解︰
An autopoietic machine is a machine organized (defined as a unity) 【整體】 as a network of processes of production (transformation and destruction) of components which: (i) through their interactions and transformations continuously regenerate 【再生】and realize 【實現】 the network of processes (relations) that produced them; and (ii) constitute 【組構】it (the machine) as a concrete unity in space in which they (the components) exist by specifying the topological domain of its realization as such a network.
就在於他們想界定『生命』和『非生命』形式的『區分特徵』，聚焦於『生命最小系統』 ── a living cell 活體細胞 ──，又用著眾多術語︰
【整體同一性】 Unity
【自主性】 Autonomy
【關係】 Relation
【網絡】 Complex Network
【轉化】 Transformation
【拓撲學】 Topology
以及與『生命系統』關聯的『複雜系統』理化現象︰
假 使微觀的描述活體细胞的理化現象，構成細胞的分子組件，形成了『新陳代謝』分子組件之『生產/破壞網絡』之『過程關係』。同時被產生的某些分子組件在物理 空間裡建立了『邊界』boundary ，這個『細胞膜』分割出了系統之『內‧外』能量/物質交換界面，而且某些被產生之分子組件『再生』了那個『製造它們』的『生產/破壞網絡』，所以在『自我 平衡』狀態下，保有了『整體』的『同一性』。
於是細胞也就有了『形狀』，這個形狀是由『生產/破壞網絡』的功能維繫『拓樸性』所決定的。因此『抽象』的來說︰
『自生自成』機器的核心在於組件生毀之『網絡組織』以及通過組件『過程』實現與再生該組件生毀之『網絡組織』的循環系統。
□︰先有雞，還是先有蛋？
○︰同時生成，是雞蛋也！
雞蛋生能生雞蛋之雞；
雞生能生雞之雞蛋而已！！
───
完成於創造『自生成』並行『微自控機』之後，───
《 M♪o 之 TinyIoT 《起承轉合》之未來鳥瞰！！》
祿
取之無窮之井水，享用不盡的福氣。
《説文解字》：祿，福也。从示彔聲。
《清明》 唐‧杜牧
清明時節雨紛紛，
路上行人欲斷魂。
借問酒家何處有？
牧童遙指杏花村。
對『認知』 cognition 的『再次』 re- 之『認知』 cognition 就是『辨識』 re-cognition 。在一篇名為《 Dynamic Causal Models and Autopoietic Systems 》的摘要裡︰
ABSTRACT
Dynamic Causal Modelling (DCM) and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems 【自組織系統】like the brain. DCM has been developed recently by the neuroimaging 【神經成像】community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models) for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism 【經驗論】of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic 【腦電圖】signals acquired during amygdala 【杏仁核】stimulation in an epileptic 【癲癇】patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated.
Key terms: Dynamic Causal Modelling, brain functional organization, plasticity, autonomous systems, autopoiesis.
說明了這篇論文的內容。人們的目光開始轉向『既古又新』之現象『徵候系統』。
左手畫出正畫出左手的右手
右手完成將完成右手之左手
生命機器
Autopoietic Systems
圖靈機
在計算機科學的領域裡，自然也有人專注於『自生自成機器』應當如何建立『形式理論』 formal theory？這個機器可以用『圖靈機』來『模擬』的嗎？就像這篇《 Towards Autopoietic Computing 》文章所說的︰ A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system’s circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people’s work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.
在此僅摘要兩小段，略窺『自我生成』之『計算』的旨趣︰
1 Introduction
Natural systems provide unique examples of computation, in a form very different from contemporary computer architectures. Biology also demonstrates capabilities such as adaptation, self-repair and self-organisation that are becoming increasingly desirable for our technology [1]. Autopoietic systems (auto = self and poiesis = generating or producing) as a theoretical construct on the nature of living systems centre on two main notions: that of the circular organisation of metabolism and a redefinition of the systemic concepts of structure and organisation. This theoretical construct has found an important place in theoretical biology, but it could also be used as a foundation for a new type of computing. We provide a summary of autopoietic theory, before discussing the development
of autopoietic computation [17]. …
3.2 Computability
Autopoietic systems are intrinsically different from Turing machines, the structure of which is shown in Figure 3. They cannot be simulated by Turing machines as they are not Turing-computable, for the following reason. The self-referential nature of circularity that characterises autopoietic systems leads to the dynamic creation of an unpredictable number of states. According to [29, 30, 18], the dynamic creation of an unpredictable number of new states implies that no upper bound can be placed on the number of states required. As the Church definition of computability assumes that the basic operations of a system must be finite, e.g. recursive, the Church-Turing thesis7 cannot be applied. Hence, autopoietic systems are non-Turing-computable This is difficult to prove using only the elements of autopoietic theory [23, 22], but it is claimed [18] to flow trivially from the inclusion of autopoietic systems in (M,R) systems.8 The non-computability of autopoietic systems [16, 3] suggests (yet to be proven) that some intrinsic and fundamental part of their behaviour escapes our standard analysis based on phase states and/or evolution equations.
……
恐先生才是貴人多忘事的哩！一時口呆無辯，但思那時代果真了解『太一生水』之旨的耶︰
一個『活細胞』的『理化反應網絡』創生了『細胞膜』，分開了『自我』與外在『世界』，內部之『自我生成』的『組織』以及『機制』，『再生』且 『實現』了『生命』！在這個『意義』上，馬圖拉那說︰
Living is cognition
生活即是認知
但是那個『小細胞』能夠承擔這個『大責任』嗎？假使我們問那個形貌多變的『小細胞』之『空間維度』是『幾何』？？又該是如何計算的呢！更難知由『細胞共和國』所組成之『生命尺度』的了！！
數一數，究竟『人類細胞有多少』的呀？？