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 cmbRb 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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Atkinson RC, Shiffrin RM (1968) Human memory: a proposed system and its control processes. Psychol Learn Motiv 2:89–195
Baddeley AD (2001) Is working memory still working? Am Psychol 56(11):851
Bose SK, Lawrence CP, Liu Z, Makarenko KZ, van Damme RMJ, Broersma HJ, van der Wiel WG (2005) Evolution of a designless nanoparticle network into reconfigurable Boolean logic. Nat Nanotechnol 10(12):1048–1052. https://doi.org/10.1038/NNANO.2015.207
Bruss D et al (2004) Distributed quantum dense coding. Phys Rev Lett 93:210501
Cosgrove KP, Calrolyne MM, Staley JK (2007) Evolving knowledge of sex differences in brain structure, function, and chemistry. Biol Psychiatry 62(8):847–855
Craik FI, Lockhart RS (1972) Levels of processing: a framework for memory research. J Verbal Learn Verbal Behav 11(6):671–684
Friedman AS (2002) The fundamental distinction between brains and turing machines. Berkeley Sci J 6(1):28–33
Haranas I, Gkigkitzis I (2013) The number of information bits related to the minimum quantum and gravitational masses in a vacuum dominated universe. Astrophys Space Sci 346:213–218. https://doi.org/10.1007/s10509-013-1434-1.
Haranas I, Gkigkitzis I, Kotsireas I, Austerlitz C (2016) Neuronal correlation parameter and the idea of thermodynamic entropy of an N-body gravitationally bounded system, GENEDIS 2016, genetics and neurodegeneration. Adv Exp Med Biol 987:35–44. http://web.mit.edu/asf/www/PopularScience/FriedmanBrainsAndTuringMachines2002.pdf
Mesulam MM (1998) From sensation to cognition. Brain 121:1013–1052
Morris CD, Bransford JD, Franks JJ (1977) Levels of processing versus transfer appropriate processing. J Verbal Learn Verbal Behav 16(5):519–533
Neumann J (2012) The computer and the brain, 3rd edn. Yale University Press
Rumelhart DE, McClelland JL, PDP Research Group (1988) Parallel distributed processing, vol 1. IEEE, pp 354–362
Saslaw WC (1987) Gravitational physics of stellar and galactic systems. In: Cambridge monographs on mathematical physics. Cambridge University Press, Cambridge, UK, pp 245–248
Shoshani J, Kupsky WJ, Marchant GH (2006) Elephant brain. Part I: Gross morphology functions, comparative anatomy, and evolution. Brain Res Bulletin 70:124–157
Wang H, Wang B, Normoyle KP, Jackson K, Spitler K, Sharrock FM, Miller MC, Best C, Llano D, Du R (2014) Brain temperature and its fundamental properties: a review for clinical neuroscientists. Front Neurosci 8:307
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Haranas, I., Gkigkitzis, I., Georgiadis, S., McLeod, D. (2020). Number of Brain States in an N-Body Dynamical Scenario According to the Universal Bekenstein Entropy Bound. In: Vlamos, P. (eds) GeNeDis 2018. Advances in Experimental Medicine and Biology, vol 1196. Springer, Cham. https://doi.org/10.1007/978-3-030-32637-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-32637-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32636-4
Online ISBN: 978-3-030-32637-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)