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Instantaneous Communication Between Cerebellum, Hypothalamus, and Hippocampus (C–H–H) During Decision-Making Process in Human Brain-III

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Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering

Abstract

Using the available database of cerebellum, hypothalamus, and hippocampus (C–H–H) architecture of the human brain, we rebuilt the structures theoretically and experimentally using a similar dielectric material and studied their resonant communication. We also replicated humanoid brain circuits: language and conversation, thinking and intelligence, emotion, love, fear, threat, hunger and pain, and memory using special cables and Yagi antenna junctions. By comparing the experimental responses of three different brain components of the humanoid bot’s functional circuits, we have identified that resonance frequencies vary widely. However, there are similarities in the ratio of resonance frequencies. Thus, a geometric language would probably be the governing machine language of future humanoid bots.

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References

  1. Wang, S., et al.: Numerical simulation and analysis of effects of individual differences on the field distribution in the human brain with electromagnetic pulses. Sci. Rep. 11, 16504 (2021)

    Article  Google Scholar 

  2. Wanger, C., et al.: The electric field distribution in the brain during TT Fields therapy and its dependence on tissue dielectric properties and anatomy: a computational study. Phys Med Biol. 60(18), 7339–7357 (2015)

    Article  Google Scholar 

  3. Singh, P., Ray, K., Fujita, D., Bandyopadhyay, A.: Complete dielectric resonator model of human brain from MRI data: a journey from connectome neural branching to single protein. In: Ray K., Sharan S., Rawat S., Jain S., Srivastava S., Bandyopadhyay A. (eds.) Engineering Vibration, Communication and Information Processing. Lecture Notes in Electrical Engineering, vol. 478, pp.717–733. Springer, Singapore (2019)

    Google Scholar 

  4. Singh, P., Sahoo, P., Ray, K., Ghosh, S., Bandyopadhyay, A.: Building a non-ionic, non-electronic, non-algorithmic artificial brain: cortex and connectome interaction in a Humanoid Bot Subject (HBS). In: Kaiser M.S., Bandyopadhyay A., Mahmud M., Ray K. (eds.), Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol. 1309, pp. 245–278. Springer, Singapore (2021)

    Google Scholar 

  5. Alekseichuk, I. et al.: Electric field dynamics in the brain during multi-electrode transcranial electric stimulation. Nat. Commun. 10(2573) (2019)

    Google Scholar 

  6. Qiu, L.B., et al.: Effects of electromagnetic pulse on blood-brain barrier permeability and tight junction proteins in rats. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi 27(9), 539–543 (2009)

    Google Scholar 

  7. Wang, Q., et al.: To study of dose-response relationship of pulsed electromagnetic radiation on rat blood-brain-barrier. Chin. J. Dis. Control Prevent. 7(13), 401–404 (2003)

    Google Scholar 

  8. Miranda, P.C., et al.: Predicting the electric field distribution in the brain for the treatment of glioblastoma. Phys. Med. Biol. 59(15), 4137–4147 (2014)

    Article  Google Scholar 

  9. Wenger, C., Salvador, R., Basser, P. J., Miranda, P.C.: Modeling tumor treating fields (TTFields) application within a realistic human head model. Annu. Int. Conf. IEEE Eng Med Biol Soc. PMID- 26736813, 2555–2558 (2015)

    Google Scholar 

  10. Lin, J.C., Wu, C.L., Lam, C.K.: Transmission of electromagnetic pulse into the head. Proc. IEEE 63, 1726–1727 (1975)

    Article  Google Scholar 

  11. Wang, S., Song, Z.G., Wu, D.C., Pu, Y.R.: Numerical simulation and analysis of the effect of individual differences on the field distribution in human brain with electromagnetic pulse. Sci. Rep. 11, 16504 (2021)

    Article  Google Scholar 

  12. Basar, E.: Chaotic dynamics and resonance phenomena in brain function: progress, perspectives and thoughts. In: Basar, E. (ed.) Chaos in Brain Function, pp. 1–30. Springer, Heidelberg (1990)

    Chapter  Google Scholar 

  13. Saxena, K., Singh, P., Sahoo, P., Sahu, S., Ghosh, S., Ray, K., Fujita, D., Bandyopadhyay, A.: Fractal, scale free electromagnetic resonance of a single brain extracted microtubule nanowire, a single tubulin protein and a single neuron. Fractal Fract. 4(11), 1–16 (2020)

    Google Scholar 

  14. Buzsáki, G., Draguhn, A.: Neuronal oscillations in cortical networks. Science 304(5679), 1926–1929 (2004)

    Article  Google Scholar 

  15. Buzsaki, G.: Rhythms of the Brain. Oxford University Press, New York (2006)

    Book  MATH  Google Scholar 

  16. Goyal, A., et al.: Functionally distinct high and low theta oscillations in the human hippocampus. Nat. Commun. 11(1), 2469 (2020)

    Article  Google Scholar 

  17. Lu, H., Hartmann, M.J., Bower, J.M.: Correlations between purkinje cell single-unit activity and simultaneously recorded field potentials in the immediately underlying granule cell layer. J. Neurophysiol. 94(3), 1849–1860 (2005)

    Article  Google Scholar 

  18. Courtemanche, R., Pellerin, J.P., Lamarre, Y.: Local field potential oscillations in primate cerebellar cortex: modulation during active and passive expectancy. J. Neurophysiol. 88(2), 771–782 (2002)

    Article  Google Scholar 

  19. Hartmann, M.J., Bower, J.M.: Oscillatory activity in the cerebellar hemispheres of unrestrained rats. J. Neurophysiol. 80(3), 1598–1604 (1998)

    Article  Google Scholar 

  20. D’Angelo, E., Nieus, T., Maffei, A., Armano, S., Rossi, P., Taglietti, V., Fontana, A., Naldi, G.: Theta-frequency bursting and resonance in cerebellar granule cells: experimental evidence and modeling of a slow k+-dependent mechanism. J. Neurosci. 21(3), 759–770 (2001)

    Article  Google Scholar 

  21. Middleton, S.J., et al.: High-frequency network oscillation in cerebellar cortex. Neuron 58(5), 763–774 (2008)

    Article  Google Scholar 

  22. Jacobs, J.: Hippocampal theta oscillations are slower in humans than in rodents: implications for models of spatial navigation and memory. Philos. Trans. R. Soc. Lond. B Biol. Sci. 369(1635), 20130304 (2014)

    Article  Google Scholar 

  23. Maidenbaum, S., et al.: Grid-like hexadirectional modulation of human entorhinal theta oscillations. Proc. Natl. Acad. Sci. 115(42), 10798–10803 (2018)

    Article  Google Scholar 

  24. Hasselmo, M.E. et al.: Theta rhythm and the encoding and retrieval of space and time. Neuroimage 85, 656–66 (2014).

    Google Scholar 

  25. Lega, C.B., et al.: Human hippocampal theta oscillations and the formation of episodic memories. Hippocampus 22(4), 748–761 (2012)

    Article  Google Scholar 

  26. Saleem, S.N., et al.: Lesions of the hypothalamus: MR imaging diagnostic features. Radio Graph. 27(4), 1087–1108 (2007)

    Google Scholar 

  27. Stanley, S., et al.: Bidirectional electromagnetic control of the hypothalamus regulates feeding and metabolism. Nature 531, 647–650 (2016)

    Article  Google Scholar 

  28. Fujisawa, I.: Magnetic resonance imaging of the hypothalamic-neurohypophyseal system. J. Neuroendocrinol. 16(4), 297–330 (2004)

    Article  Google Scholar 

  29. Ono, D., Yamanaka, A.: Hypothalamic regulation of the sleep/wake cycle. Neurosci. Res. 118, 74–81 (2017)

    Article  Google Scholar 

  30. Hamani, C. et al.: Memory enhancement induced by hypothalamic/fornix deep brain stimulation. Ann Neurol. 63, 119–123 (2008)

    Google Scholar 

  31. CST Studio Suite. CST Studio Suite 3D EM Simulation and Analysis Sofware Network. https://www.3ds.com/products-services/simulia/products/cst-studio-suite/

  32. Singh, P., Saxena, K., Singhania, A., Sahoo, P., Ghosh, S., Chhajed, R., Ray, K., Fujita, D., Bandyopadhyay, A.: A self-operating time crystal model of the human brain: can we replace entire brain hardware with a 3D fractal architecture of clocks alone? Information 11(5), 238 (2020)

    Article  Google Scholar 

  33. Llinas, R.R., Walton, K.D., Lang, E.J. (2004) Cerebellum. In: Shepherd, G.M. (eds). The Synaptic Organization of the Brain. Oxford University Press, New York (2004)

    Google Scholar 

  34. Eccles, J.C.: Review lecture: the cerebellum as a computer: patterns in space and time. J. Phygiol. 229, 1–32 (1973)

    Google Scholar 

  35. Reddy, S., et al.: A brain-like computer made of time crystal: could a metric of prime alone replace a user and alleviate programming forever? In: Ray, K., Pant, M., Bandyopadhyay, A. (eds.) Soft Computing Applications. Studies in Computational Intelligence, vol. 761, pp. 1–43. Springer, Singapore (2018)

    Chapter  Google Scholar 

  36. Ringo, J.L., Doty, R.W., Demeter, S., Simard, P.Y.: Time is of the essence: a conjecture that hemispheric specialization arises from interhemispheric conduction delay. Cereb Cortex 4, 331–343 (1967)

    Article  Google Scholar 

  37. Matthews, P.C., Strogatz, S.H.: Phase diagram for the collective behavior of limit-cycle oscillators. Phys. Rev. Lett. 64, 1701–1704 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  38. Wang, X.J.: Multiple dynamical modes of thalamic relay neurons: rhythmic bursting and intermittent phase-locking. Neuroscience 59, 21–31 (1994)

    Article  Google Scholar 

  39. Graybiel, A.M.: The basal ganglia: learning new tricks and loving it. Curr. Opin. Neurobiol. 15, 638–644 (2005)

    Article  Google Scholar 

  40. Kamondi, A., Acsády, L., Wang, X.J., Buzsáki, G.: Theta oscillations in somata and dendrites of hippocampal pyramidal cells in vivo: activity-dependent phase-precession of action potentials. Hippocampus 8, 244–261 (1998)

    Article  Google Scholar 

  41. Harris, K.D., Henze, D.A., Hirase, H., Leinekugel, X., Dragoi, G., Czurkó, A., Buzsáki, G.: Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells. Nature 417, 738–741 (2002)

    Article  Google Scholar 

  42. Lytton, W.W., Sejnowski, T.J.: Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. J. Neurophysiol. 66, 1059–1079 (1991)

    Article  Google Scholar 

  43. Royer, S., et al.: Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat. Neurosci. 15, 769–775 (2012)

    Article  Google Scholar 

  44. Bliss, T.V., Collingridge, G.L.: A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39 (1993)

    Article  Google Scholar 

  45. Buzsáki, G. et al.: Oscillatory and intermittent synchrony in the hippocampus: rele-vance to memory trace formation. In: Buzsáki G., Llinás R., Singer W., Berthoz A., Christen Y. (eds.), Temporal Coding in the Brain. Research and Perspectives in Neuro-sciences. pp. 145–175. Springer, Berlin (1994)

    Google Scholar 

  46. Leutgeb, S., Leutgeb, J.K., Barnes, C.A., Moser, E.I., McNaughton, B.L., Moser, M.B.: Independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science 309, 619–623 (2005)

    Article  Google Scholar 

  47. Ferbinteanu, J., Shapiro, M.L.: Prospective and retrospective memory coding in the hippocampus. Neuron 40, 1227–1239 (2003)

    Article  Google Scholar 

  48. Wallenstein, G.V., Eichenbaum, H., Hasselmo, M.E.: The hippocampus as an associator of discontinuous events. Trends Neurosci. 21, 317–323 (1998)

    Article  Google Scholar 

  49. Jonas, P., Bischofberger, J., Fricker, D., Miles, R.: Interneuron diversity series: Fast in, fast out—temporal and spatial signal processing in hippocampal interneurons. Trends Neurosci. 27, 30–40 (2004)

    Article  Google Scholar 

  50. Buño, W., Jr., Velluti, J.C.: Relationships of hippocampal theta cycles with bar pressing during self-stimulation. Physiol. Behav. 19, 615–621 (1977)

    Article  Google Scholar 

  51. Cenquizca, L.A., Swanson, L.W.: Spatial organization of direct hippocampal field CA1 axonal projections to the rest of the cerebral cortex. Brain Res. Rev. 56, 1–26 (2007)

    Article  Google Scholar 

  52. Ciocchi, S., et al.: Brain computation. Selective information routing by ventral hippocampal CA1 projection neurons. Science 348, 560–563 (2015)

    Google Scholar 

  53. Rotstein, H.G., Pervouchine, D.D., Acker, C.D., Gillies, M.J., White, J.A., Buhl, E.H., Whittington, M.A., Kopell, N.: Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network. J. Neurophysiol. 94, 1509–1518 (2005)

    Article  Google Scholar 

  54. Berry, S.D., Thompson, R.F.: Prediction of learning rate from the hippocampal electroencephalogram. Science 200, 1298–1300 (1978)

    Article  Google Scholar 

  55. Carter, R.: The Human Brain Book: An Illustrated Guide to its Structure, Function, and Disorders. DK: London, UK (2014)

    Google Scholar 

  56. Bandyopadhyay, A.: In Nanobrain. The Making of an Artificial Brain from a Time Crystal, 1st edn. Taylor & Francis Inc., Bosa Roca (2020)

    Google Scholar 

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Acknowledgements

The authors acknowledge the Asian Office of Aerospace R&D (AOARD) a part of United States Air Force (USAF) for the grant no. FA2386-16-1-0003 (2016-2019) on electromagnetic resonance-based communication and intelligence of biomaterials.

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Singh, P. et al. (2022). Instantaneous Communication Between Cerebellum, Hypothalamus, and Hippocampus (C–H–H) During Decision-Making Process in Human Brain-III. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_8

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