On human consciousness: A mathematical perspective
On human consciousness: A mathematical perspective
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DOI：
10.1162/netn_a_00030
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发表时间：
2018-01-01
影响因子：
4.7
通讯作者：
Grindrod, Peter
中科院分区：
文献类型：
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作者：
Grindrod, Peter
我们认为，大型模块化神经元到神经元动态网络的数学建模和分析的影响。我们解释了相对较小规模的强连接网络的动态行为如何自然地导致非二进制信息处理，从而导致多个假设决策，即使在大脑结构的最低水平。反过来，我们在这些想法的基础上，解决意识这一难题的某些方面。这些包括在具有基本决策和单元处理器分类层的架构中如何产生感觉。我们讨论了如何提出一个“双层次模型”，由外部感知的，日益复杂的物理元素，和内部经历的，心理元素（我们认为是等同于感情），可能会支持学习和不断发展的意识方面。我们引入了这样一种观点，即人类大脑应该能够随意地重新唤起主观的心理感受，因此这些感受不能依赖于内部的喋喋不休或内部不稳定驱动的活动（模式）。基于动力学系统和非二进制信息处理的这个模型的直接结果是，有限的人类大脑必须总是学习和遗忘，任何可能的主观内部感觉，可能是完全理想化的可数无限的方面，永远不会被僵尸或自动机完全先验地学习。进化中的人类大脑可能会越来越充分地体验到它（但永远不会完全体验到，甚至在一生中也不会）。我们认为，在我们的模型中，心理元素和内部模式（感觉）在处理和决策过程中扮演着类似于潜在变量的角色，从而赋予了进化的“快速思考”优势。
We consider the implications of the mathematical modeling and analysis of large modular neuron-to-neuron dynamical networks. We explain how the dynamical behavior of relatively small-scale strongly connected networks leads naturally to nonbinary information processing and thus to multiple hypothesis decision-making, even at the very lowest level of the brain's architecture. In turn we build on these ideas to address some aspects of the hard problem of consciousness. These include how feelings might arise within an architecture with a foundational decision-making and classification layer of unit processors. We discuss how a proposed "dual hierarchy model," made up from both externally perceived, physical elements of increasing complexity, and internally experienced, mental elements (which we argue are equivalent to feelings), may support aspects of a learning and evolving consciousness. We introduce the idea that a human brain ought to be able to reconjure subjective mental feelings at will, and thus these feelings cannot depend on internal chatter or internal instability-driven activity (patterns). An immediate consequence of this model, grounded in dynamical systems and nonbinary information processing, is that finite human brains must always be learning and forgetting and that any possible subjective internal feeling that might be fully idealized with a countable infinity of facets could never be learned completely a priori by zombies or automata. It may be experienced more and more fully by an evolving human brain (yet never in totality, not even in a lifetime). We argue that, within our model, the mental elements and thus internal modes (feelings) play a role akin to latent variables in processing and decision-making, and thus confer an evolutionary "fast-thinking" advantage.