Suppr超能文献

根据普遍的贝肯斯坦熵边界,N 体动力场景中的脑状态数量。

Number of Brain States in an N-Body Dynamical Scenario According to the Universal Bekenstein Entropy Bound.

机构信息

Department of Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON, Canada.

NOVA, Department of Mathematics, Annandale, VA, USA.

出版信息

Adv Exp Med Biol. 2020;1196:19-32. doi: 10.1007/978-3-030-32637-1_3.

Abstract

There is an intense interest in the modulation of brain neural circuits and its correlations with different behavioral states, memory, learning, as well as neuropsychological disorders. It is believed that brain cells form functional circuits, process information and mediate behavior. Therefore, the brain system may be thought of as a super-computing machine that turns information into thoughts, memories, and cognitions. Moreover, according to the quantum brain dynamics and quantum conscience hypotheses, quantum theory, the most fundamental theory of matter, may help explain the function of the brain. In the intersection of the architecture of the brain's biological substrate, the processing of information and entropy (as a measure of information processing capacity), and the generation of input to this system (either externally or internally), one may expect to find the foundations of cognition and behavior as an emergent phenomenon. In this chapter, we calculate the entropy Bekenstein bound of the brain, and from that the number of information N in bits that is required to describe the brain down to its tiniest detail. Furthermore, we define the quantity cmR as brain quantum of action b. Next, we estimate the possible number of states b in the human brain as related to the number of information bits N. Furthermore, we derive an expression for the kinetic energy of a pair of neurons as a function of brain temperature T, the number of information N in bits, and the neuron mass mn as well as the number density of neurons n. We introduce the conjecture that the time rate of r(t) might represent the velocity at which a pair of neurons can approach or recede from each other upon experiencing a transfer of N number of information bits.

摘要

人们对大脑神经回路的调制及其与不同行为状态、记忆、学习以及神经心理障碍的相关性非常感兴趣。人们认为脑细胞形成功能性回路,处理信息并介导行为。因此,可以将大脑系统视为一台将信息转化为思想、记忆和认知的超级计算机。此外,根据量子脑动力学和量子意识假说,物质最基本的理论——量子理论可能有助于解释大脑的功能。在大脑生物基质的架构、信息处理和熵(作为信息处理能力的度量)以及该系统输入的产生(无论是外部的还是内部的)的交叉点上,可以预期在认知和行为的涌现现象中找到基础。在这一章中,我们计算了大脑的贝肯斯坦熵界限,并由此得出了描述大脑最微小细节所需的信息量 N。此外,我们将 cmR 定义为大脑量子作用 b。接下来,我们估计了人类大脑中 b 的可能状态数与信息量 N 之间的关系。此外,我们还推导出了一对神经元的动能表达式,它是大脑温度 T、信息量 N 以及神经元质量 mn 和神经元密度 n 的函数。我们提出了一个猜想,即 r(t) 的时间率可能代表一对神经元在经历 N 个信息量的传输时可以相互接近或远离的速度。

相似文献

2
Importance of quantum decoherence in brain processes.
Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000 Apr;61(4 Pt B):4194-206. doi: 10.1103/physreve.61.4194.
4
Quantum-like model of brain's functioning: decision making from decoherence.
J Theor Biol. 2011 Jul 21;281(1):56-64. doi: 10.1016/j.jtbi.2011.04.022. Epub 2011 May 7.
5
Quantum-like behavior without quantum physics II. A quantum-like model of neural network dynamics.
J Biol Phys. 2018 Dec;44(4):501-538. doi: 10.1007/s10867-018-9504-9. Epub 2018 Jun 8.
6
Entropy Balance in the Expanding Universe: A Novel Perspective.
Entropy (Basel). 2019 Apr 17;21(4):406. doi: 10.3390/e21040406.
7
Minimum energy surface required by quantum memory devices.
Phys Rev Lett. 2013 Jun 21;110(25):250502. doi: 10.1103/PhysRevLett.110.250502. Epub 2013 Jun 18.
8
Consciousness, biology and quantum hypotheses.
Phys Life Rev. 2012 Sep;9(3):285-94. doi: 10.1016/j.plrev.2012.07.001. Epub 2012 Jul 10.
9
Partial quantum information.
Nature. 2005 Aug 4;436(7051):673-6. doi: 10.1038/nature03909.
10
Bits and q-bits as versatility measures.
An Acad Bras Cienc. 2004 Jun;76(2):425-8. doi: 10.1590/s0001-37652004000200035. Epub 2004 Jun 8.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验