Quantum Brain Dynamics and Virtual Reality

IF 2 4区 生物学 Q2 BIOLOGY
Akihiro Nishiyama , Shigenori Tanaka , Jack A. Tuszynski
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引用次数: 0

Abstract

In this paper we propose a control theory of manipulating holograms in Quantum Brain Dynamics (QBD) involving our subjective experiences, i.e. qualia. We begin with the Lagrangian density in QBD and extend our theory to a hierarchical model involving multiple layers covering the neocortex. We adopt reservoir computing approach or morphological computation to manipulate waveforms of holograms involving our subjective experiences. Numerical simulations performed indicate that the convergence to target waveforms of holograms is realized by external electric fields in QBD in a hierarchy. Our theory can be applied to non-invasive neuronal stimulation of the neocortex and adopted to check whether or not our brain adopts the language of holography. In case the protocol in a brain is discovered and the brain adopts the language of holography, our control theory will be applied to develop virtual reality devices by which our subjective experiences provided by the five senses in the form of qualia are manipulated non-invasively. Then, the information content of qualia might be directly transmitted into our brain without passing through sensory organs.

量子大脑动力学与虚拟现实
在本文中,我们提出了一种在量子脑动力学(QBD)中操纵全息图的控制理论,其中涉及我们的主观体验,即质点。我们从 QBD 中的拉格朗日密度入手,将理论扩展到涉及覆盖新皮层的多层次模型。我们采用水库计算方法或形态学计算来处理涉及我们主观体验的全息图波形。数值模拟表明,全息图目标波形的收敛是通过分层 QBD 中的外部电场实现的。我们的理论可应用于对新皮层神经元的非侵入性刺激,并用于检查我们的大脑是否采用了全息语言。如果发现了大脑中的协议,并且大脑采用了全息语言,那么我们的控制理论将被应用于开发虚拟现实设备,通过这种设备,我们可以非侵入性地操纵由五官提供的以 "质点"(qualia)形式存在的主观体验。届时,"质点 "的信息内容可能会不经感觉器官直接传入我们的大脑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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