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The Experience-Dependent Dynamics of Human Consciousness

机译:人类意识的经验依赖动力学

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By reviewing most of the neurobiology of consciousness, this article highlights some major reasons why a successful emulation of the dynamics of human consciousness by artificial intelligence is unlikely. The analysis provided leads to conclude that human consciousness is epigenetically determined, experience, and context-dependent at the individual level. It is subject to changes in time that are essentially unpredictable. If cracking the code to human consciousness were possible, the result would most likely have to consist of a temporal pattern code simulating long-distance signal reverberation and de-correlation of all spatial signal contents from temporal signals. In the light of the massive evidence for complex interactions between implicit (non-conscious) and explicit (conscious) contents of representation, the code would have to be capable of making implicit (non-conscious) processes explicit. It would have to be capable of a progressively less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure identical to that of the human brain, from the synaptic level to that of higher cognitive functions. The code’s activation thresholds would depend on specific temporal signal coincidence probabilities, vary considerably with time and across individual experience data, and would therefore require dynamically adaptive computations capable of emulating the properties of individual human experience. No known machine or neural network learning approach has such potential.
机译:通过回顾大多数意识神经生物学,本文重点介绍了一些主要原因,说明人工智能不太可能成功地模仿人类意识的动力学。提供的分析得出的结论是,人类意识是在个体层面上由表观遗传决定的,经验且取决于上下文。它会随着时间的变化而变化,这基本上是无法预测的。如果有可能将代码破解为人类意识,则结果很可能必须包含一个时间模式代码,该代码模拟长距离信号混响和所有空间信号内容与时间信号的去相关。鉴于大量的证据表明表示的隐式(非意识)和显式(意识)内容之间存在复杂的交互作用,因此代码必须能够使隐式(非意识)过程显式化。从突触水平到更高的认知功能,它必须能够在与人脑相同的,不断发展的神经网络结构中,逐渐地选择越来越少的时间活动模式。该代码的激活阈值将取决于特定的时间信号重合概率,并随时间和个体经验数据而变化很大,因此将需要能够模拟个体人类经验属性的动态自适应计算。没有已知的机器或神经网络学习方法具有这种潜力。

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