1. Home
  2. Issues
  3. Volume 30, Issue 4 (2019)
  4. Conceptual Modelling of Brain State Dyna ...

Informatica


Conceptual Modelling of Brain State Dynamics as Free Energy and Entropy-Based Processes
Volume 30, Issue 4 (2019), pp. 749–780
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 March 2019
Accepted
1 November 2019
Published
1 January 2019

Abstract

Despite the mass of empirical data in neuroscience and plenty of interdisciplinary approaches in cognitive science, there are relatively few applicable theories of how the brain as a coherent system functions in terms of energy and entropy processes. Recently, a free energy principle has been portrayed as a possible way towards a unified brain theory. However, its capacity, using free energy and entropy, to unify different perspectives on brain function dynamics is yet to be established. This multidisciplinary study attempts to make sense of the free energy and entropy not only from the perspective of Helmholtz thermodynamic basic principles but also from the information theory framework. Based on the proposed conceptual framework, we constructed (i) four basic brain states (deep sleep, resting, active wakeful and thinking) as dynamic entropy and free energy processes and (ii) stylized a self-organizing mechanism of transitions between the basic brain states during a day period. Adaptive transitions between brain states represent homeostatic rhythms, which produce complex daily brain states dynamics. As a result, the proposed simulation model produces different self-organized circadian dynamics of brain states for different types of chronotypes, which corresponds with the empirical observations.

References

 
Borbely, A.A. (1982). A two process model of sleep regulation. Human Neurobiology, 1(3), 195–204.
 
Borbely, A.A., Daan, S., Wirz-Justice, A., Deboer, T. (2016). The two-process model of sleep regulation: a reappraisal. Journal of Sleep Research, 25(2), 131–143.
 
Borbély, A.A., Achermann, P. (1999). Sleep homeostasis and models of sleep regulation. Journal of Biological Rhythms, 14(6), 557–568.
 
Booth, V., Diniz Behn, C.G. (2014). Physiologically-based modelling of sleep-wake regulatory networks. Mathematical Biosciences, 250, 54–68.
 
Brillouin, L. (1953). The negentropy principle of information. Journal of Applied Physics, 24(9), 1152–1163.
 
Brown, R.E., Basheer, R., McKenna, J.T., Strecker, R.E., McCarley, R.W. (2012). Control of sleep and wakefulness. Physiological Reviews, 92(3), 1087–1187.
 
Buzsaki, G. (2011). Rhythms of the Brain, 1st ed. Oxford University Press, New York.
 
Daan, Beersma, S., Borbély A. A, D.G. (1984). Timing of human sleep: recovery process gated by a circadian pacemaker. The American Journal of Physiology, 246(2 Pt 2), R161–R183.
 
Dayan, P. (1998). Recurrent sampling models for the Helmholtz machine. Neural Computation, 11(3), 653–677.
 
Dijk, D.J. (1999). Circadian variation of EEG power spectra in NREM and REM sleep in humans: dissociation from body temperature. Journal of Sleep Research, 8(3), 189–195.
 
Ekman, P., Cordaro, D.T. (2011). What is meant by calling emotions basic. Emotion Review, 3(4), 364–370.
 
Friston, K. (2010). The free-energy principle: a unified brain theory? Nature reviews Neuroscience, 11(2), 127.
 
Friston, K., Kilner, J., Harrison (2006). A free energy principle for the brain. Journal of Physiology-Paris, 100(1), 70–87.
 
Fulcher, B.D., Phillips, A.J.K., Robinson, P.A. (2008). Modelling the impact of impulsive stimuli on sleep-wake dynamics. Physical Review E, 78(5), 051920.
 
Hinton, E., Terrence, J.S. (1999). Unsupervised Learning: Foundations of Neural Computation. MIT Press.
 
Horne, J.A., Ostberg, O. (1976). A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms. International Journal of Chronobiology, 4(2), 97–110.
 
Huang, G. (2008). Is this a unified theory of the brain? New Scientist, 2658, 30–33.
 
Kamiński, B. (2015). Interval metamodels for the analysis of simulation input-output relations. Simulation Modelling Practice and Theory, 54, 86–100. https://doi.org/10.1016/j.simpat.2015.03.008.
 
Kezys, D., Plikynas, D. (2014). Prognostication of human brain EEG signal dynamics using a refined coupled oscillator energy exchange model. NeuroQuantology, 12(4), 337–349. https://doi.org/10.14704/nq.2014.12.4.779.
 
Kleitman, N. (1963). Sleep and Wakefulness. University of Chicago Press, Chicago, IL.
 
Kronauer, R.E., Czeisler, C.A., Pilato, S.F., Moore-Ede, M.C., Weitzman, E.D. (1982). Mathematical model of the human circadian system with two interacting oscillators. American Journal of Physiology, 242(1), R3–R17.
 
Libet, B. (2006). Reflections on the interaction of the mind and brain. Progress in Neurobiology, 78(3–5), 322–326.
 
McCarley, R.W., Hobson, J.A. (1975). Neuronal excitability modulation over the sleep cycle: a structural and mathematical model. Science, 189(4196), 58–60.
 
McCarley, R.W., Massaquoi, S.G. (1986). A limit cycle mathematical model of the REM sleep oscillator system. American Journal of Physiology, 251, R1011–R1029.
 
McFadden, J. (2002). The conscious electromagnetic information (CEMI) field theory. Journal of Consciousness Studies, 9(8), 45–60.
 
Medeiros, A.L.D., Mendes, D.B.F., Lima, Araujo J. F, P.F. (2001). The relationships between sleep-wake cycle and academic performance in medical students. Biological Rhythm Research, 32(2), 263–270.
 
Möller-Levet, C.S., Archer, S.N., Bucca, G., Laing, E.E., Slak, A., Kabiljo, R., Dijk, D.J. (2013). Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptome. Proceedings of the National Academy of Sciences, 110(12), E1132–E1141.
 
Nielsen, L.S., Danielsen, Sørensen T. I. A, K.V. (2011). Short sleep duration as a possible cause of obesity: critical analysis of the epidemiological evidence. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity, 12(2), 78–92.
 
Pessa, E., Vitiello, G. (2004). Quantum noise induced entanglement and chaos in the dissipative quantum model of brain. International Journal of Modern Physics B, 18(06), 841–858.
 
Phillips, A.J.K., Robinson, P.A. (2007). A quantitative model of sleep-wake dynamics based on the physiology of the brainstem ascending arousal system. Journal of Biological Rhythms, 22(2), 167–179.
 
Phillips, A.J.K., Robinson, P.A. (2008). Sleep deprivation in a quantitative physiologically based model of the ascending arousal system. Journal of Theoretical Biology, 255(4), 413–423.
 
Phillips, A.J., Chen, P.Y., Robinson, P.A. (2010). Probing the mechanisms of chronotype using quantitative modeling. Journal of Biological Rhythms, 25(3), 217–227.
 
Phillips, A.J.K., Czeisler, Klerman E. B, C.A. (2011). Revisiting spontaneous internal desynchrony using a quantitative model of sleep physiology. Journal of Biological Rhythms, 26(5), 441–453.
 
Pitkänen, M. (2006). Negentropy Maximization Principle. TGD Inspired Theory of Consciousness. Onlinebook. http://tgd.wippiespace.com/public_html/tgdconsc/tgdconsc.html#nmpc.
 
Plikynas, D. (2015). Oscillating agent model: quantum approach. NeuroQuantology, 13(1), 20–34.
 
Plikynas, D. (2016). Introducing the Oscillations Based Paradigm: The Simulation of Agents and Social Systems. Springer, Switzerland. https://doi.org/10.1007/978-3-319-39040-6.
 
Plikynas, D., Bašinskas, G., Laukaitis, A. (2014a). Towards oscillations-based simulation of social systems: a neurodynamic approach. Connection Science, 27(2), 188–211. https://doi.org/10.1080/09540091.2014.956293.
 
Plikynas, D., Masteika, S., Basinskas, G., Kezys, D., Kumar, P., Laukaitis, A. (2014b). Social systems in terms of coherent individual neurodynamics: conceptual premises and experimental scope. International Journal of General Systems, 43(5), 434–469. https://doi.org/10.1080/03081079.2014.888552.
 
Plutchik, R. (2002). Nature of emotions. American Scientist, 89(4), 349.
 
Pribram, K.H. (1999). Quantum holography: is it relevant to brain function? Information Sciences, 115(1–4), 97–102.
 
Prerau, M.J., Brown, R.E., Bianchi, M.T., Ellenbogen, J.M., Purdon, P.L. (2016). Sleep neurophysiological dynamics through the lens of multitaper spectral analysis. Physiology, 32(1), 60–92.
 
Refinetti, R., Cornélissen, G., Halberg, F. (2007). Procedures for numerical analysis of circadian rhythms. Biological Rhythm Research, 38(4), 275–325.
 
Reilly, T., Bambaeichi, E. (2003). Methodological issues in studies of rhythms in human performance. Biological Rhythm Research, 34(4), 321–336.
 
Roenneberg, T., Wirz-Justice, A., Merrow, M. (2003). Life between clocks: daily temporal patterns of human chronotypes. Journal of Biological Rhythms, 18(1), 80–90.
 
Russell, J.A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172.
 
Russell, J.A., Barrett, L.F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. Journal of Personality and Social Psychology, 76(5), 805–819.
 
Skeldon, A.C., Dijk, D.J., Derks, G. (2014). Mathematical models for sleep-wake dynamics: comparison of the two-process model and a mutual inhibition neuronal model. PLoS ONE, 9(8).
 
Skeldon, A.C., Phillips, A.J., Dijk, D.J. (2017). The effects of self-selected light-dark cycles and social constraints on human sleep and circadian timing: a modeling approach. Scientific Reports, 7, 45158.
 
Strogatz, S.H. (1987). Human sleep and circadian rhythms: a simple model based on two coupled oscillators. Journal of Mathematical Biology, 25(3), 327–347.
 
Thaheld, F.H. (2005). An interdisciplinary approach to certain fundamental issues in the fields of physics and biology: towards a unified theory. Biosystems, 80(1), 41–56.
 
Thatcher, R.W. (2010). Validity and reliability of quantitative electroencephalography. Journal of Neurotherapy, 14(2), 122–152.
 
Tononi, G., Cirelli, C. (2006). Sleep function and synaptic homeostasis. Sleep Medicine Reviews, 10, 49–62.
 
Tononi, G., Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron, 81, 12–34.
 
Travis, F., Arenander, A. (2006). Cross-sectional and longitudinal study of effects of transcendental meditation practice on interhemispheric frontal asymmetry and frontal coherence. The International Journal of Neuroscience, 116(12), 1519–1538.
 
Travis, F., Orme-Johnson, D.W. (1989). Field model of consciousness: EEG coherence changes as indicators of field effects. The International Journal of Neuroscience, 49(3–4), 203–211.
 
Vinne, V., Zerbini, G., Siersema, A., Pieper, A., Merrow, M., Hut, R.A., Roenneberg, T., Kantermann, T. (2015). Timing of examinations affects school performance differently in early and late chronotypes. Journal of Biological Rhythms, 30(1), 53–60. https://doi.org/10.1177/0748730414564786.
 
Vitiello, P.D.G. (2001). My Double Unveiled: The Dissipative Quantum Model of Brain. John Benjamins Publishing Company, Amsterdam; Philadelphia, PA.
 
Yik, M., Russell, J.A., Steiger, J.H. (2011). A 12-point circumplex structure of core affect. Emotion, 11(4), 705.

Biographies

Plikynas Darius
darius.plikynas@mii.vu.lt

D. Plikynas is affiliated as a professor and senior research fellow at Vilnius University, Institute of Data Science and Digital Technologies. He has published 2 monographs, 8 chapters in books, over 40 publications and over 50 conference papers. His main field of interest includes but is not limited to the fundamental and applied research (modelling and simulation) of individual cognitive behaviour and distributed social processes. It covers interdisciplinary research domains in natural and social sciences, e.g. computational intelligence, agent based or multi-agent simulations, complexity research, artificial life, neuroscience, physics, information theory, social networks, distributed cognition, etc.

Sakalauskas Leonidas

L. Sakalauskas is affiliated as a prof. habil. dr. and chief research fellow at Vilnius University, Institute of Data Science and Digital Technologies. His field of research concerns optimization, data mining, big data analysis, modelling of sustainable development. He has published over 240 papers. He is the leader of the EU working group of stochastic programming, 15 PhD students have been guided by him.

Raudys Aistis

A. Raudys is affiliated as an assoc. prof. at Mathematics and Informatics Faculty in Vilnius University. He has been doing research related with artificial intelligence, agent-based systems, big data analysis, etc. He has published over 30 articles and over 40 conference papers.


Full article Related articles PDF XML
Full article Related articles PDF XML

Copyright
© 2019 Vilnius University
Open access article under the CC BY license.

Keywords
free energy entropy brain states circadian rhythms homeostasis chronotypes

Funding
This research was funded by a grant (No. P-MIP-17-368) from the Research Council of Lithuania.

Metrics
since January 2020
1297

Article info
views

834

Full article
views

632

PDF
downloads

273

XML
downloads


Share


RSS