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arXiv 1609.04033q-bio.NC
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对人类意识

On Human Consciousness

Peter Grindrod

摘要:

我们认为神经元到神经元动态复杂网络的数学分析的影响。我们展示了小规模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,从而多个假设决策,即使在最低层次的大脑结构。反过来,我们建立在这些想法,以解决意识的难题。我们讨论了如何提出的“双层次模型”,由外部感知,物理,日益复杂的元素,和内部心理元素(经验)的形式,可能会支持学习和不断发展的意识。我们讨论的想法,人类的大脑应该能够重新变戏法主观的心理感受,因此这些不能依赖于内部的鼻子(喋喋不休)或内部不稳定驱动的活动。基于动力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或遗忘,任何可能的主观内在感觉,可能是理想化的可数无限的方面,永远不能被僵尸或自动机学习:尽管它可以被进化中的大脑越来越充分地体验到(但永远不会完全体验到,甚至一生都不会)。

英文摘要:

We consider the implications of the mathematical analysis of neurone-to-neurone dynamical complex networks. We show how the dynamical behaviour of small scale strongly connected networks lead naturally to non-binary information processing and thus multiple hypothesis decision making, even at the very lowest level of the brain's architecture. In turn we build on these ideas to address the hard problem of consciousness. We discuss how a proposed "dual hierarchy model", made up form of both external perceived, physical, elements of increasing complexity, and internal mental elements (experiences), may support a leaning and evolving consciousness. We discuss the idea that a human brain ought to be able to re-conjure subjective mental feelings at will and thus these cannot depend on internal nose (chatter) or internal instability-driven activity. An immediate consequence of this model, grounded in dynamical systems and non-binary information processing, is that finite human brains must always be learning or forgetteing and that any possible subjective internal feeling that may be idealised with a countable infinity of facets, can never be learned by zombies or automata: though it can be experienced more and more fully by an evolving brain (yet never in totality, not even in a lifetime).

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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