意识和认知的要素。生物学、数学、物理学与泛心论：信息拓扑学视角
Elements of Consciousness and Cognition. Biology, Mathematic, Physics and Panpsychism: an Information Topology Perspective
摘要：
这篇评论介绍了最近和旧的结果，基本的定量和定性方面的意识和认知，并解决问题“什么是意识？“从生物学、神经科学认知、物理学和数学的角度结合起来。提出用信息拓扑的方法统一各种结果和理论。第一章介绍了神经系统的各种组织尺度上的基本知觉的假设和结果，并提出了一个假设，即意识的电动内在本质是由一个类比代码维持的。它强调了维持感知和意识动态的学习机制的多样性，包括多尺度的适应和稳态过程。第二章考察了认知和意识的逻辑方面，并提出了一个基于测度和概率论的公理化。拓扑和建设性的逻辑提供了一个内在的概率逻辑，长期的目标是避免悖论的分解诱导的选择公理。我们勾勒出一个基本的程序，允许一个数学公式的信息表达一个拉哥德尔。然后，我们提出了信息拓扑的形式主义，并提出它提供了一个初步的基础，在一个正式的完形理论的认知和意识的主要模型进行综合。信息拓扑学建立了信息理论功能的表征，允许精确表达信息结构和模式。它提供了一个量化的统计相互作用的结构和他们的统计物理学和机器学习方面的表达。值得注意的是，这些拓扑学方法允许调和一些主要的意识理论。
英文摘要：
This review presents recent and older results on elementary quantitative and qualitative aspects of consciousness and cognition and tackles the question "What is consciousness?" conjointly from biological, neuroscience-cognitive, physical and mathematical points of view. It proposes to unify various results and theories by means of information topology. The first chapter presents the postulates and results on elementary perception at various organizational scales of the nervous system and proposes the hypothesis of an electrodynamic intrinsic nature of consciousness which is sustained by an analogical code. It underlines the diversity of the learning mechanisms that sustain the dynamics of perception and consciousness, including adaptive and homeostatic processes on multiple scales. The second chapter investigates the logical aspects of cognition and consciousness and proposes an axiomatization based on measure and probability theory. Topos and constructive logic are presented as providing an intrinsic probabilistic logic, with the long-term aim of avoiding the paradoxical decomposition induced by the Axiom of Choice. We sketch an elementary procedure allowing an expression of the information of a mathematical formula a la Godel. We then present the formalism of information topology and propose that it provides a preliminary basis for synthesizing the main models of cognition and consciousness within a formal Gestalt theory. Information topology establishes a characterization of information theory functions, allowing for a precise expression of information structures and patterns. It provides a quantification of the structure of statistical interactions and their expression in terms of statistical physics and machine learning. Notably, those topological methods allow conciliation of some of the main theories of consciousness.
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