Skip to main content
SpringerLink
Log in

Revisiting Self-Operating Mathematical Universe (SOMU) as a Theory for Artificial General Intelligence, AGI and G+ Consciousness

  • Chapter
  • First Online:
Brain-like Super Intelligence from Bio-electromagnetism

Part of the book series: Studies in Rhythm Engineering ((SRE))

Abstract

SOMU is a theory of artificial general intelligence, AGI, that proposes a system with a universal code embedded in it, allowing it to interact with the environment and adapt to new situations without programming. So far, the whole universe and human brain have been modelled using SOMU. Each brain element forms a cell of a fractal tape, a cell possessing three qualities: obtaining feedback from the entire tape (S), transforming multiple states simultaneously (R), and bonding with any cell-states within-and-above the network of brain components. The undefined and non-finite nature of the cells rejects the tuples of a Turing machine. SRT triplets extend the brain’s ability to perceive natural events beyond the spatio-temporal structure, using a cyclic sequence or loop of changes in geometric shapes. This topology factor, becomes an inseparable entity of space–time, i.e. space–time-topology (STt). The fourth factor, prime numbers, can be used to rewrite spatio-temporal events by counting singularity regions in loops of various sizes. The pattern of primes is called a phase prime metric, PPM that links all the symmetry breaking rules, or every single phenomenon of the universe. SOMU postulates space–time-topology-prime (STtp) quatret as an invariant that forms the basic structure of information in the brain and the universe, STtp is a bias free, attribution free, significance free and definition free entity. SOMU reads recurring events in nature, creates 3D assembly of clocks, namely polyatomic time crystal, PTC and feeds those to PPM to create STtps. Each layer in a within-and-above brain circuit behaves like an imaginary the world, generating PTCs. These PTCs of different imaginary worlds interact through a new STtp tensor decomposition mathematics. Unlike string theory, SOMU proposes that the fundamental elements of the universe are helical or vortex phases, not strings. It dismisses string theory's approach of using sum of 4 × 4 and 8 × 8 tensors to create a 12 × 12 tensor for explaining universe. Instead, SOMU advocates a network of multinion tensors ranging from 2 × 2 to 108 × 108 in size. With just 108 elements, a system can replicate ~90 of all symmetry breaking rules in the universe, allowing a small systemic part to mirror majority events of the whole, that is human level consciousness G. Under the SOMU model, for a part to be conscious, it must mirror a significant portion of the whole and should act as a whole for the abundance of similar mirroring parts within itself.

Correspondence and requests for materials should be addressed to A. B. anirban.bandyo@gmail.com and or anirban.bandyopadhyay@nims.go.jp.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agrawal L, Chhajed R, Ghosh S, Ghosh B, Ray K, Sahu S, Fujita D, Bandyopadhyay A (2018) Fractal Information Theory (FIT)-derived Geometric Musical Language (GML) for brain-inspired hypercomputing. Soft Comput: Theor Appl 548:343–372

    Google Scholar 

  2. Agrawal L, Ghosh S, Ghosh B, Ray K, Sahu S, Fujita D, Bandyopadhyay A (2016) Replacing turing tape with a fractal tape: a new information theory, associated mechanics and decision making without computing, consciousness (Chapter 6). In: Integrating Indian and western perspective, pp 87–159

    Google Scholar 

  3. Agrawal L, Sahu S, Ghosh S, Shiga T, Fujita D, Bandyopadhyay A (2016) Inventing atomic resolution scanning dielectric microscopy to see a single protein complex operation live at resonance in a neuron without touching or adulterating the cell. J Integr Neurosci 15(04):435–462

    Article  Google Scholar 

  4. Aiello A, Banzer P, Neugebauer M et al (2015) From transverse angular momentum to photonic wheels. Nat Photon 9:789–795. https://doi.org/10.1038/nphoton.2015.203

    Article  Google Scholar 

  5. Alikhani P, Brunner N, Crépeau C et al (2021) Experimental relativistic zero-knowledge proofs. Nature 599:47–50. https://doi.org/10.1038/s41586-021-03998-y

  6. Atanasov V, Dandoloff R (2008) Curvature-induced quantum behaviour on a helical nanotube. Phys Lett A 372:6141–6144

    Article  Google Scholar 

  7. Baars BJ (2005) Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. In: The boundaries of consciousness: neurobiology and neuropathology. Progress in brain research, vol 150, pp 45–53. CiteSeerX 10.1.1.456.2829. https://doi.org/10.1016/S0079-6123(05)50004-9. ISBN 9780444518514. PMID 16186014.

  8. Baars BJ (2017) The global workspace theory of consciousness: predictions and results. In: Schneider S, Velmans M (eds) The Blackwell companion to consciousness, 2nd ed. Wiley-Blackwell. https://doi.org/10.1002/9781119132363.ch16. ISBN 978-0-470-67406-2

  9. Baars BJ (2019) A cognitive theory of consciousness. In: Demertzi A et al (ed) Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv 5:eaat7603

    Google Scholar 

  10. Babbush R et al (2014) Construction of energy functions for lattice heteropolymer models: efficient encodings for constraint satisfaction programming and quantum annealing. Adv Chem Phys 155:201–243. https://doi.org/10.1002/9781118755815

    Article  Google Scholar 

  11. Bandyopadhyay A (2020) Nanobrain: the making of an artificial brain from a time crystal. CRC Press Taylor and Francis. https://doi.org/10.1201/9780429107771

    Book  Google Scholar 

  12. Bandyopadhyay A, Fujita D (2021) Electromagnetic device, magnetic and electrical vortex synthesis device and magnetic and optical vortex synthesis device; Application no. JP 2021-172702

    Google Scholar 

  13. Bandyopadhyay A, Fujita D, Pati R (2009) Architecture of a massive parallel processing nano brain operating 100 billion molecular neurons simultaneously. Int J Nanotech Mol Comput 1:50–80

    Article  Google Scholar 

  14. Bandyopadhyay A, Ghosh S, Fujita D (2019). Universal geometric-musical language for big data processing in an assembly of clocking resonators, JP-2017-150171, 8/2/2017: World patent received February 2019, WO 2019/026983

    Google Scholar 

  15. Bandyopadhyay A, Ghosh S, Fujita D (2020) Human brain like intelligent decision-making machine; JP-2017-150173; 8/2/2017; World patent WO 2019/026984; US Patent App. 16/635,892

    Google Scholar 

  16. Bandyopadhyay A, Ghosh S, Fujita D, Pati R, Sahu S (2011) An advanced architecture of a massive parallel processing nano brain operating 100 billion molecular neurons simultaneously. In: Mclennan B (ed) Theoretical and technological advancements in nanotechnology and molecular computation: interdisciplinary gains, pp 43–73. https://doi.org/10.4018/978-1-60960-186-7.ch004

  17. Bandyopadhyay A, Miki K, Wakayama Y (2006) Writing and erasing information in multilevel logic systems of a single molecule using scanning tunneling microscope. Appl Phys Lett 89(24):243506

    Article  Google Scholar 

  18. Bandyopadhyay A, Nittoh K, Wakayama Y, Yagi S, Miki K (2006) Global tuning of local molecular phenomena: an alternative approach to bionanoelectronics. J Phys Chem B 110(42):20852–20857

    Article  Google Scholar 

  19. Bandyopadhyay A, Sahoo P, Fujita D (2021) Self-learning by information processing device and self-learning for information processing method Application: No. 2021-172703; Filing Date: 2021-10-21

    Google Scholar 

  20. Bandyopadhyay A, Sahu S, Fujita D (2009) Smallest artificial molecular neural-net for collective and emergent information processing. Appl Phys Lett 95(11):113702

    Article  Google Scholar 

  21. Bandyopadhyay A, Sahu S, Fujita D, Wakayama Y (2010) A new approach to extract multiple distinct conformers and co-existing distinct electronic properties of a single molecule by point-contact method. Phys Chem Chem Phys 12(9):2198–2208

    Article  Google Scholar 

  22. Bandyopadhyay A, Wakayama Y (2007) Origin of negative differential resistance in molecular Junctions of Rose Bengal. Appl Phys Lett 90(2):023512

    Article  Google Scholar 

  23. Bar-Yosef A (2001) Musical time organization and space concept: a model of cross-cultural analogy. Ethnomusicology 45(3):423–442 (20 pp). University of Illinois Press

    Google Scholar 

  24. Basak S, Nanda J, Banerjee A (2012) A new aromatic amino acid based organogel for oil spill recovery. J Mater Chem 22:11658–11664. https://doi.org/10.1039/C2JM30711A

    Article  Google Scholar 

  25. Battiston F, Amico E, Barrat A et al (2021) The physics of higher-order interactions in complex systems. Nat Phys 17:1093–1098. https://doi.org/10.1038/s41567-021-01371-4

    Article  Google Scholar 

  26. Bayne T, Hohwy J, Owen AM (2016). Are there levels of consciousness? Trends Cogn Sci 20:405–413

    Google Scholar 

  27. Biamonte JD, Love PJ (2007) Realizable Hamiltonians for universal adiabatic quantum computers. Phys Rev A 78:1–7. https://doi.org/10.1103/PhysRevA.78.012352

    Article  MathSciNet  Google Scholar 

  28. Boly M et al (2011) Preserved feedforward but impaired top-down processes in the vegetative state. Science 332:858–862

    Google Scholar 

  29. Brea J, Gerstner W (2016) Does computational neuroscience need new synaptic learning paradigms? Curr Opin Behav Sci 11:61–66

    Article  Google Scholar 

  30. Buhlmann P (2018) Invariance in heterogeneous, large-scale and high-dimensional data. In: Proceedings of international congress of mathematicians, pp 2785–2800. https://doi.org/10.1142/9789813272880_0160

  31. Butzenberger K (1996) The doctrine of doubt and the reference of terms in Indian grammar logic and philosophy of language. J Indian Philos 24:363–406

    Article  Google Scholar 

  32. Choi J, Ju S (2019) Properties of the geometric phase in electromechanical oscillations of carbon-nanotube-based nanowire resonators. Nanoscale Res Lett 14:44. https://doi.org/10.1186/s11671-019-2855-8

    Article  Google Scholar 

  33. Christian D (2011) Maps of time: an introduction to big history. University of California Press. ISBN 978-0-520-95067-2

    Google Scholar 

  34. Criscione JC, Humphrey JD, Douglas AS, Hunter WC (2000) An invariant basis for natural strain which yields orthogonal stress response terms in isotropic hyperelasticity. J Mech Phys Solids 48(12):2445–2465

    Google Scholar 

  35. Dicke RH (1981) Interaction‐free quantum measurements: a paradox? Am J Phys. Am Assoc Phys Teach (AAPT) 49(10):925–930. Bibcode:1981AmJPh..49..925D. https://doi.org/10.1119/1.12592, ISSN 0002-9505

  36. Ennis DB, Kindlmann G (2006) Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn Reson Med 55:136–146. https://doi.org/10.1002/mrm.20741

    Article  Google Scholar 

  37. Escolanoa F, Hancockb ER, Lozanoa MA, Curado M (2017) The mutual information between graphs. Pattern Recogn Lett 87:12–19

    Article  Google Scholar 

  38. Friston KJ (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11:127–138

    Google Scholar 

  39. Ganeri J (1996) The Hindu syllogism: nineteenth-century perceptions of Indian logical thought. Philos East West 46(1):1–16

    Article  Google Scholar 

  40. Ganeri J (2001) Indian logic: a reader. Routledge, Richmond, Surrey, p vii. ISBN 9781136119385

    Google Scholar 

  41. Gao XC, Xu JB, Qian TZ (1991) Geometric phase and the generalized invariant formulation. Phys Rev A 44(11):7016–7021. https://doi.org/10.1103/physreva.44.7016

    Article  MathSciNet  Google Scholar 

  42. Gennaro RJ (1996) Consciousness and self-consciousness: A defense of the higher-order thought theory of consciousness. Volume 6 of advances in consciousness research, John Benjamins Publishing. ISBN(s) 9789027251268 9027299846 1556191863 9027251266

    Google Scholar 

  43. Gennaro RJ (ed) (2004) Higher-order theories of consciousness. Amsterdam and Philadelphia: John Benjamins Publishers.

    Google Scholar 

  44. Ghosh S, Dutta M, Ray K, Fujita D, Bandyopadhyay A (2016) A simultaneous one pot synthesis of two fractal structures via swapping two fractal reaction kinetic states. Phys Chem Chem Phys 18:14772–14775

    Article  Google Scholar 

  45. Ghosh S, Dutta M, Sahu S, Fujita D, Bandyopadhyay A (2014) Nano molecular-platform: a protocol to write energy transmission program inside a molecule for bio-inspired supramolecular engineering. Adv Funct Mater 24(10):1364–1371

    Article  Google Scholar 

  46. Ghosh S, Fujita D, Bandyopadhyay A (2015) An organic jelly made fractal logic gate with an infinite truth table. Sci Rep 5(1):1–8

    Article  Google Scholar 

  47. Ghosh S, Sahu S, Fujita D, Bandyopadhyay A (2014) Design and operation of a brain like computer: a new class of frequency-fractal computing using wireless communication in a supramolecular organic, inorganic systems. Information 5:28–99

    Article  Google Scholar 

  48. Ghosh S, Singh P, Manna J, Saxena K, Sahoo P, Krishnanda SD, Ray K, Hill JP, Bandyopadhyay A (2022) The century-old picture of a nerve spike is wrong: filaments fire, before membrane. Commun Integr Biol 15(1):115–120. https://doi.org/10.1080/19420889.2022.2071101

    Article  Google Scholar 

  49. Goodfellow I, Lee H, Le Q, Saxe A, Ng A (2009) Measuring invariances in deep networks. In: Advances in neural information processing systems (NIPS 2009), vol 22, pp 646–654. https://papers.nips.cc/paper/2009/hash/428fca9bc1921c25c5121f9da7815cde-Abstract.html

  50. Hadsell R, Chopra S, LeCun Y (2006) Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR'06), pp 1735–1742. https://doi.org/10.1109/CVPR.2006.100

  51. Hancock SW, Zahedpour S, Goffin A, Milchberg HM (2019) Free-space propagation of spatiotemporal optical vortices. Optica 6:1547–1553

    Article  Google Scholar 

  52. Harish R (2019) Nasadiya Shukta- The Hymm of Creation in the Rig Veda. RV 10.154; RV 10.190. https://www.speakingtree.in/blog/nasadiya-suktam-the-hymn-of-creation-in-the-rig-veda-734806

  53. Herzberg G, Longuet-Higgins HC (1963) Intersection of potential energy surfaces in polyatomic molecules. Discuss Faraday Soc 35:77–82. https://doi.org/10.1039/DF9633500077

  54. Hinton GE (2007) Learning multiple layers of representation. Trends Cogn Sci 11(10):428–434

    Google Scholar 

  55. Honglak L, Ranganath G, Ng A (2009) Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML '09: proceedings of the 26th annual international conference on machine learning, pp 609–616. https://doi.org/10.1145/1553374.1553453

  56. Howard SR, Avarguès-Weber A, Garcia JE, Greentree AD, Dyer AG (2019) Numerical cognition in honeybees enables addition and subtraction. Sci Adv 5(2):eaav0961. https://doi.org/10.1126/sciadv.aav0961

  57. Huang C, Chen X, Oladipo A et al (2015) Generation of subwavelength plasmonic nanovortices via helically corrugated metallic nanowires. Sci Rep 5:13089. https://doi.org/10.1038/srep13089

    Article  Google Scholar 

  58. Jedamzik K, Pogosian L (2020) Relieving the hubble tension with primordial magnetic fields. Phys Rev Lett 125:181302

    Article  Google Scholar 

  59. Jha G (1999) Nyaya-Sutras of Gautama. Motilal Banarsidass 4(1). ISBN 978-81-208-1264-2

    Google Scholar 

  60. Kharel SR, Mezei TR, Chung S et al (2021) Degree-preserving network growth. Nat Phys 18:100–106. https://doi.org/10.1038/s41567-021-01417-7

    Article  Google Scholar 

  61. King RD et al (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427:247–252

    Article  Google Scholar 

  62. King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, Clare A (2009) The automation of science. Science 324(5923):85–89. https://doi.org/10.1126/science.1165620

    Article  Google Scholar 

  63. Kitano H (2021) Nobel turing challenge: creating the engine for scientific discovery. NPJ Syst Biol Appl 7:29. https://doi.org/10.1038/s41540-021-00189-3

  64. Kobayashi T, Sloth MS (2019) Early cosmological evolution of primordial electromagnetic fields. Physical Review D 100(2):023524. https://doi.org/10.1103/PhysRevD.100.023524

    Article  MathSciNet  Google Scholar 

  65. Kohji T, Haruhisa K, Satoshi M (2002) Graph automata: natural expression of self-reproduction. Phys D 171(4):197–210

    Article  MathSciNet  Google Scholar 

  66. Lau H, Rosenthal D (2011) Empirical support for higher-order theories of conscious awareness. Trends Cogn Sci 15:365–373

    Google Scholar 

  67. Lauber H, Weidenhammer P, Dubbers D (1994) Geometric phases and hidden symmetries in simple resonators. Phys Rev Lett 72(7):1004–1007. https://doi.org/10.1103/PhysRevLett.72.1004

    Article  Google Scholar 

  68. Liboff AR (2016) Magnetic correlates in electromagnetic consciousness. Electromagn Biol Med 35(3):228–236. https://doi.org/10.3109/15368378.2015.1057641. Epub 2016 Apr 6. PMID: 27049696.

  69. Liu YY, Slotine JJ, Barabási AL (2011) Controllability of complex networks. Nature 473:167–173. https://doi.org/10.1038/nature10011

    Article  Google Scholar 

  70. Lobanov AP, Zensus JA (2001) A cosmic double helix in the archetypical quasar 3C273. Science 294(5540):128–131. https://doi.org/10.1126/science.1063239

    Article  Google Scholar 

  71. Maron H, Fetaya E, Segol N, Lipman Y (2019) On the universality of invariant networks. In: Proceedings of the 36th international conference on machine learning. PMLR 97. https://arxiv.org/pdf/1901.09342.pdf

  72. Mohanty JN (1970) Nyāya theory of doubt. In: Phenomenology and ontology. Phaenomenologica, vol 37, pp 198–219. https://doi.org/10.1007/978-94-010-3252-0_18

  73. Mukherjee R, Ghosh K, Chakrabarty S (2020) On the unifying nature of vibration. Int J Appl Phys (SSRG-IJAP) 7(1):134–141. https://doi.org/10.14445/23500301/IJAP-V7I1P119

  74. Pattanayak A et al (2022). Cognitive engineering for AI: an octave drawing test for building a mathematical structure of a subconscious mind. In: Kaiser MS, Ray K, Bandyopadhyay A, Jacob K, Long KS (eds) Proceedings of the third international conference on trends in computational and cognitive engineering. Lecture notes in networks and systems, vol 348, pp 135–148. https://doi.org/10.1007/978-981-16-7597-3_11

  75. Pockett S (2000) The nature of consciousness. ISBN 978-0-595-12215-8

    Google Scholar 

  76. Pockett S (2012) The electromagnetic field theory of consciousness. J Conscious Stud 19(11–12):191–223.

    Google Scholar 

  77. Pramanik S, Singh P, Sahoo P, Ray K, Bandyopadhyay A (2023) 1D to 20D tensors like dodecanions and Icosanions to model human cognition as morphogenesis in the density of primes. In: M.S. Kaiser et al (eds) Proceedings of the fourth international conference on trends in computational and cognitive engineering, TCCE 2022. Lecture notes in networks and systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_38

  78. Raayoni G, Gottlieb S, Manor Y, Pisha G, Harris Y, Mendlovic U, Haviv D, Hadad Y, Kaminer I (2021) Generating conjectures on fundamental constants with the Ramanujan machine. Nature 590(7844):67–73. https://doi.org/10.1038/s41586-021-03229-4

    Article  Google Scholar 

  79. Reddy S, Sonker D, Singh P, Saxena K, Singh S, Chhajed R, Tiwari S, Karthik KV, Ghosh S, Ray K, Bandyopadhyay A (2018) A brain-like computer made of time crystal: could a metric of prime alone replace a user and alleviate programming forever? Soft Comput Appl 761:1–43

    Google Scholar 

  80. Reimann MW, Nolte M, Scolamiero M, Turner K, Perin R, Chindemi G, Dłotko P, Levi R, Hess K, Markram H (2017) Cliques of neurons bound into cavities provide a missing link between structure and function. Front Comput Neurosci 11:48. https://doi.org/10.3389/fncom.2017.00048.PMID:28659782;PMCID:PMC5467434

    Article  Google Scholar 

  81. Rhodes N, Willett P, Calvet A, Dunbar JB, Humblet C (2003) CLIP: similarity searching of 3D databases using clique detection. J Chem Inf Comput Sci 43(2):443–448. https://doi.org/10.1021/ci025605o

    Article  Google Scholar 

  82. Richerme P et al (2013) Experimental performance of a quantum simulator: optimizing adiabatic evolution and identifying many-body ground states. Phys Rev A 88:12334. https://doi.org/10.1103/PhysRevA.88.012334

    Article  Google Scholar 

  83. Roland J, Cerf NJ (2022) Quantum search by local adiabatic evolution. Phys Rev A 65:42308. https://doi.org/10.1103/Phys-RevA.65.042308

    Article  Google Scholar 

  84. Rosenblatt F (1961) Perceptrons and the theory of brain mechanics. Cornell Aeronautical Lab Inc., VG-1196-G, p 621

    Google Scholar 

  85. Sahoo P, Singh P, Manna J, Singh RP, Hill J.P, Nakayama T, Ghosh S, Bandyopadhyay A (2023) A third angular momentum of photons. Symmetry 15:158. https://doi.org/10.3390/sym15010158

  86. Sahoo P, Singh P, Saxena K, Ghosh S, Singh RP, Benosman R, Hill JP, Nakayama T, Bandyopadhyay A (2023) A general-purpose organic gel computer that learns by itself. Neuromorph Comput Eng 3 044007

    Google Scholar 

  87. Sahu et al (2013) Multi-level memory-switching properties of a single brain microtubule. Appl Phys Lett 102:123701

    Article  Google Scholar 

  88. Sahu et al (2013) Atomic water channel controlling remarkable properties of a single brain microtubule: correlating single protein to its supramolecular assembly. Biosens Bioelectron 47:141–148

    Article  Google Scholar 

  89. Sahu S, Fujita D, Bandyopadhyay A (2015) US patent 9019685B2; Sahu S, Fujita D, Bandyopadhyay A (2010) Inductor made of arrayed capacitors (2010) Japanese patent has been issued on 20th August 2015 JP-511630 (world patent filed, this is the invention of fourth circuit element), US patent has been issued 9019685B2, 28th April 2015

    Google Scholar 

  90. Sahu S, Fujita D, Bandyopadhyay A (2010) An inductor made of arrayed capacitors. JP-511630; US 9019685B2, 2015. European patent EP2562776B1. https://patents.google.com/patent/EP2562776A1/de

  91. Saxena K, Karthik KV, Kumar S, Fujita D, Bandyopadhyay A (2019) Wireless communication through microtubule analogue device: noise-driven machines in the bio-systems. Eng Vib Commun Inf Process 478:735–749

    Google Scholar 

  92. Saxena K, Kumar M, Daya KS, Bandyopadhyay A (2019) Detection of milimeter wave properties of beta amyloid using dielectric filled truncated cylindrical waveguide. In: 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), pp 1–4. https://doi.org/10.23919/URSIAP-RASC.2019.8738768

  93. Saxena K, Singh P, Sahoo P, Ghosh S, Krishnanda D, Ray K, Fujita D, Bandyopadhyay A (2022) All basics that are wrong with the current concept of time crystal: learning from the polyatomic time crystals of protein, microtubule, and neuron. Proc Trends Electron Health Inf 376:243–254

    Article  Google Scholar 

  94. Saxena K, Singh P, Sahoo P, Sahu S, Ghosh S, Ray K, Fujita D, Bandyopadhyay A (2020) Fractal, scale free electromagnetic resonance of a single brain extracted microtubule nanowire, a single tubulin protein and a single neuron. Fractal Fract 4(2):11

    Article  Google Scholar 

  95. Saxena K, Singh P, Sarkar J, Sahoo P, Ghosh S, Bandyopadhyay A (2022) Polyatomic time crystals of the brain neuron extracted microtubule are projected like a hologram meters away. J Appl Phys 132:194401. https://doi.org/10.1063/5.0130618

  96. Singh P, Doti R, Lugo JE, Faubert J, Rawat S, Ghosh S, Ray K (2018) DNA as an electromagnetic fractal cavity resonator: its universal sensing and fractal antenna behavior. Soft Comput: Theories Appl 584:213–223

    Google Scholar 

  97. Singh P, Doti R, Lugo JE, Faubert J, Rawat S, Ghosh S, Ray K, Bandyopadhyay A (2018) Frequency fractal behavior in the retina nano-center-fed dipole antenna network of a human eye. Soft Comput: Theories Appl 548:201–211

    Google Scholar 

  98. Singh P, Ghosh S, Sahoo P, Ray K, Fujita D, Bandyopadhyay A (2020) Reducing the dimension of a patch-clamp to the smallest physical limit using a coaxial atom probe. Prog Electromagn Res B 89:29–44

    Article  Google Scholar 

  99. Singh P, Ocampo M, Lugo JE, Doti R, Faubert J, Rawat S, Ghosh S, Ray K, Bandyopadhyay A (2018) Fractal and periodical biological antennas: hidden topologies in DNA, wasps and retina in the eye. Soft Comput Appl 761:113–130

    Google Scholar 

  100. Singh P, Sahoo P, Saxena K, Ghosh S, Sahu S, Ray K, Fujita D, Bandyopadhyay A (2021) Quaternion, octonion to dodecanion manifold: stereographic projections from infinity lead to a self-operating mathematical universe. Proceedings of international conference on trends in computational and cognitive engineering. 1169:55–77.

    Google Scholar 

  101. Singh P, Sahoo P, Saxena K, Ghosh S, Sahu S, Ray K, Fujita D, Bandyopadhyay A. (2021) A space-timetopology-prime, stTS metric for a self-operating mathematical universe uses Dodecanion geometric algebra of 2-20 D complex vectors. Proceedings of international conference on data science and applications. 148:1–31.

    Google Scholar 

  102. Singh P, Sahoo P, Saxena K, Manna JS, Ray K, Ghosh S, Bandyopadhyay A (2021) Cytoskeletal filaments deep inside a neuron are not silent: they regulate the precise timing of nerve spikes using a pair of vortices. Symmetry 13(5):821

    Google Scholar 

  103. Singh P, Ray K, Bandyopadhyay A (2022) The making of a humanoid bot using electromagnetic antenna and sensors: biological antenna to the humanoid bot. Stud Rhythm Eng 153–195. https://doi.org/10.1007/978-981-16-9677-0_5153

  104. Singh P, Ray K, Fujita D, Bandyopadhyay A (2019) Complete dielectric resonator model of human brain from MRI data: a journey from connectome neural branching to single protein. Eng Vib Commun Inf Process 478:717–733

    Google Scholar 

  105. Singh P, Sahoo P, Ghosh S, Saxena K, Manna JS, Ray K, Krishnanda SD, Poznanski RR, Bandyopadhyay A (2021) Filaments and four ordered structures inside a neuron fire a thousand times faster than the membrane: theory and experiment. J Integr Neurosci 20(4):777–790

    Article  Google Scholar 

  106. Singh P, Sahoo P, Ray K, Ghosh S, Bandyopadhyay A (2021) Building a non-ionic, non-electronic, non-algorithmic artificial brain: cortex and connectome interaction in a Humanoid Bot Subject (HBS). In: Proceedings of international conference on trends in computational and cognitive engineering, vol 1309, pp 245–278

    Google Scholar 

  107. Singh P, Sahoo P, Saxena K, Ghosh S, Sahu S, Ray K, Fujita D, Bandyopadhyay A (2021) A space-time-topology-prime, stTS metric for a self-operating mathematical universe uses Dodecanion geometric algebra of 2-20 D complex vectors. In: Proceedings of international conference on data science and applications, vol 148, pp 1–31

    Google Scholar 

  108. Singh P, Sahoo P, Saxena K, Ghosh S, Sahu S, Ray K, Fujita D, Bandyopadhyay A (2021) Quaternion, octonion to dodecanion manifold: stereographic projections from infinity lead to a self-operating mathematical universe. In: Proceedings of international conference on trends in computational and cognitive engineering, vol 1169, pp 55–77

    Google Scholar 

  109. Singh P, Sahoo P, Saxena K, Manna JS, Ray K, Ghosh S, Bandyopadhyay A (2021) Cytoskeletal filaments deep inside a neuron are not silent: they regulate the precise timing of nerve spikes using a pair of vortices. Symmetry 13(5):821

    Article  Google Scholar 

  110. Singh P, Saxena K, Sahoo P, Ghosh S, Bandyopadhyay A (2021) Electrophysiology using coaxial atom probe array: live imaging reveals hidden circuits of a hippocampal neural network. J Neurophysiol 125(6):2107–2116

    Article  Google Scholar 

  111. Singh P, Saxena K, Sahoo P, Sarkar J, Ghosh S, Ray K, Bandyopadhyay A (2022) Instantaneous communication between cerebellum, hypothalamus, and hippocampus (C–H–H) during decision-making process in human brain-III. In: Proceedings of the third international conference on trends in computational and cognitive engineering, vol 348, pp 93–110

    Google Scholar 

  112. Singh P, Saxena K, Singhania A, Sahoo P, Ghosh S, Chhajed R, Ray K, Fujita D, Bandyopadhyay A (2020) 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

    Article  Google Scholar 

  113. Singhania A, Ghosh I, Sahoo P, Fujita D, Ghosh S, Bandyopadhyay A (2020) Radio waveguide-double ratchet rotors work in unison on a surface to convert heat into power. Nano Lett 20(9):6891–6898

    Article  Google Scholar 

  114. Snášela V, Nowakováa J, Xhafab F, Barollic L (2017) Geometrical and topological approaches to big data. Futur Gener Comput Syst 67:286–296

    Article  Google Scholar 

  115. Springel V, White S, Jenkins A et al (2005) Simulations of the formation, evolution and clustering of galaxies and quasars. Nature 435:629–636. https://doi.org/10.1038/nature03597

    Article  Google Scholar 

  116. Tong DM, Sjöqvist E, Kwek LC, Oh CH, Ericsson M (2003) Relation between geometric phases of entangled bipartite systems and their subsystems. Phys Rev A 68:022106. https://doi.org/10.1103/PhysRevA.68.022106

    Article  MathSciNet  Google Scholar 

  117. Torquato S, Zhang G, Courcy-Ireland MD (2018) Uncovering multiscale order in the prime numbers via scattering. J Stat Mech: Theory Exp 2018:093401. https://doi.org/10.1088/1742-5468/aad6be

    Article  MathSciNet  Google Scholar 

  118. Veis L, Pittner J (2014) Adiabatic state preparation study of methylene. J Chem Phys 140(21):214111. https://doi.org/10.1063/1.4880755

    Article  Google Scholar 

  119. Vikshu V (1928) Sankhya Darshana (Shastri D (eds)). Kashi Sanskrit Series, vol 67. Chaukhambha prakashan, Varanasi, India

    Google Scholar 

  120. Wang S, Zhang G, Wang X, Tong Q, Li J, Ma G (2021) Spin-orbit interactions of transverse sound. Nat Commun 12(1):6125. https://doi.org/10.1038/s41467-021-26375-9

    Article  Google Scholar 

  121. Wheeler JA (1989) Information, physics, quantum: search for links. In: Proceedings of 3rd international symposium on foundations of quantum mechanics, pp 354–368

    Google Scholar 

  122. Williford K, Bennequin D, Friston K, Rudrauf D (2018) The projective consciousness model and phenomenal selfhood. Front Psychol 17(9):2571. https://doi.org/10.3389/fpsyg.2018.02571. PMID: 30618988; PMCID: PMC6304424

  123. Winfree A (1977) Biological rhythm research: the geometry of biological time, 2nd edn. Springer (2001)

    Google Scholar 

  124. Winfree AT (1987) When time breaks down. The three-dimensional dynamics of electrochemical waves and cardiac arrhythmias. Princeton University Press, Princeton

    Google Scholar 

  125. Wiskott L, Sejnowski T (2002) Slow feature analysis: Unsupervised learning of invariances. Neural Comput 14(4):715–770

    Article  Google Scholar 

  126. Yarotsky D (2022) Universal approximations of invariant maps by neural networks. Constr Approx 55:407–474. https://doi.org/10.1007/s00365-021-09546-1

  127. Zhang Z, Qiao X, Midya B, Liu K, Sun J, Wu T, Liu W, Agarwal R, Jornet JM, Longhi S, Litchinitser NM, Feng L (2020) Tunable topological charge vortex microlaser. Science 368:760–763

    Article  Google Scholar 

  128. Zhen Z et al (2017) Exploring generalized shape analysis by topological representations. Pattern Recogn Lett 87:177–185

    Article  Google Scholar 

Download references

Acknowledgements

We thank Martin Timms for developing DDG and artificial brain control software. Authors acknowledge the Asian office of Aerospace R&D (AOARD), a part of the United States Air Force (USAF), for Grant no. FA2386-16-1-0003 (2016–2019) on the electromagnetic resonance-based communication and intelligence of biomaterials. Authors also acknowledge the financial assistance of the Scheme for Promotion of Academic and Research Collaboration (SPARC) an MHRD, Govt of India initiative for the project titled 'Management of Fractal Time in High-level Decision Making' (Govt of India, MHRD; project number P 524; Start date: 15.03. 2019–14.03.2021; Duration:2 years).

Author information

Authors and Affiliations

  1. Amity School of Applied Science, Amity University Rajasthan, Kant Kalwar, NH-11C, Jaipur Delhi Highway, Jaipur, Rajasthan, 303007, India

    Sudeshna Pramanik

  2. Department Electronics and Electrical Communication Engineering, IIT Kharagpur, Kharagpur, West Bengal, 721302, India

    Jhimli Sarkar & Kanad Ray

  3. International Center for Materials and Nanoarchitectronics (MANA), NIMS, 1-2-1 Sengen, Tsukuba, 3050047, Ibaraki, Japan

    Pushpendra Singh & Anirban Bandyopadhyay

Authors
  1. Sudeshna Pramanik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jhimli Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pushpendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kanad Ray
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anirban Bandyopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A. B. conceptualized the idea. A. B.; J. S. and P. Si. did the human subject experiment. A. B., P. Sa. built and studied the artificial brain. S. P., P. Si., A. B built mathematical models. Then, A. B analyzed the data and wrote the paper with P. Si. Finally, K.R. verified the experimental protocol and helped in the human subject study.

Corresponding author

Correspondence to Anirban Bandyopadhyay .

Editor information

Editors and Affiliations

  1. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  2. Amity University, Jaipur, Rajasthan, India

    Kanad Ray

Ethics declarations

The authors declare they have no competing financial interests.

Appendix: Supporting Figs. 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52

Appendix: Supporting Figs. 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52

See Figs. 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52.

Fig. 27
An illustration presents tensors for prime numbers 3, 5, 7, 9, 11, 13, 17, 19, and 23. The tensors present regions with similar colors.

Tensors for prime numbers, 3, 5, 7, 9, 11, 13, 17, 19, 23. Similar color means similar terms

Full size image
Fig. 28
An illustration presents tensors for prime numbers 29, 31, and 37. There is 1 tensor for prime number 29, 3 for prime number 31, and 4 for prime number 37. The tensors present regions of similar colors.

Tensors for prime numbers, 29, 31, 37. Similar color means similar terms

Full size image
Fig. 29
An illustration presents tensors for prime numbers 41 and 43. There are 4 tensors for prime number 41 and 4 tensors for prime number 43. The tensors present multiple colors with regions of similar colors.

Tensors for prime numbers, 41, 43. Similar color means similar terms

Full size image
Fig. 30
An illustration presents tensors for prime numbers 47 and 53. There is 1 tensor for prime number 47 and 3 tensors for prime number 53. The tensors present multiple colors and regions with similar colors.

Tensors for prime numbers, 47, 53. Similar color means similar terms

Full size image
Fig. 31
An illustration presents tensors for prime numbers 59 and 61. There is 1 tensor for prime number 59 and 2 tensors for prime number 61. The tensors present multiple colors and regions of similar colors.

Tensors for prime numbers, 50, 61. Similar color means similar terms

Full size image
Fig. 32
An illustration presents 3 tensors for prime number 61. The tensors present multiple colors and regions of similar colors.

Tensors for prime numbers, 61. Similar color means similar terms

Full size image
Fig. 33
An illustration presents 3 tensors for prime number 61 and 67. There are 2 tensors for prime number 61 and 1 tensor for prime number 67. The tensors present multiple colors and regions of similar colors.

Tensors for prime numbers, 61, 67. Similar color means similar terms

Full size image
Fig. 34
An illustration presents 2 tensors for prime number 71. The tensors present multiple colors and regions of similar colors for similar terms.

Tensors for prime numbers, 71. Similar color means similar terms

Full size image
Fig. 35
An illustration presents 2 tensors for prime number 71 and 73. The tensors present multiple colors and regions of similar colors for similar terms.

Tensors for prime numbers, 71, 73. Similar color means similar terms

Full size image
Fig. 36
An illustration presents 2 tensors for prime number 73. The tensors present multiple colors and regions of similar colors for similar.

Tensors for prime numbers, 73. Similar color means similar terms

Full size image
Fig. 37
An illustration presents 2 tensors for prime number 73. The tensors present regions with similar colors for similar terms.

Tensors for prime numbers, 73. Similar color means similar terms

Full size image
Fig. 38
An illustration presents 2 tensors for prime number 79. The tensors present regions with similar colors for similar terms.

Tensors for prime numbers, 79. Similar color means similar terms

Full size image
Fig. 39
An illustration presents 2 tensors for prime number 83. The tensors present regions with similar colors for similar terms.

Tensors for prime numbers, 83. Similar color means similar terms

Full size image
Fig. 40
An illustration presents 2 tensors for prime number 83. The tensors present regions with similar colors for similar terms. Tensor 1 presents less segments and color codes than tensor 2.

Tensors for prime numbers, 83. Similar color means similar terms

Full size image
Fig. 41
An illustration presents 2 tensors for prime number 89 and 97. The tensors present regions with similar colors for similar terms. Tensor 1 presents more segments and color codes than tensor 2.

Tensors for prime numbers, 89, 97. Similar color means similar terms

Full size image
Fig. 42
A tensor for prime number 97. The tensor presents regions with similar colors for similar terms. It reads 97 equals 2 times 2 times 2 times 2 times 3 times 2 plus 1.

Tensors for prime numbers, 97. Similar color means similar terms

Full size image
Fig. 43
A tensor for prime number 101. The tensor presents regions with similar colors for similar terms. It reads 101 equals 10 times 10 plus 1.

Tensors for prime numbers, 101. Similar color means similar terms

Full size image
Fig. 44
A tensor for prime number 101. The tensor presents regions with similar colors for similar terms. It reads 101 equals 2 times 2 plus 1 times 10 times 2 plus 1.

Tensors for prime numbers, 101. Similar color means similar terms

Full size image
Fig. 45
A tensor for prime number 101. The tensor presents regions with similar colors for similar terms. It reads 101 equals 2 times 2 plus 1 times 2 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 101. Similar color means similar terms

Full size image
Fig. 46
A tensor for prime number 101. The tensor presents regions with similar colors for similar terms. It reads 101 equals 2 times 2 times 2 plus 1 times 5 times 2 plus 1.

Tensors for prime numbers, 101. Similar color means similar terms

Full size image
Fig. 47
A tensor for prime number 103. The tensor presents regions with similar colors for similar terms. It reads 103 equals 2 times 2 times 2 times 2 plus 1 times 3 times 2 plus 1.

Tensors for prime numbers, 103. Similar color means similar terms

Full size image
Fig. 48
A tensor for prime number 103. The tensor presents regions with similar colors for similar terms. It reads 103 equals 2 times 2 plus 1 times 2 times 2 plus 1 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 103. Similar color means similar terms

Full size image
Fig. 49
A tensor for prime number 103. The tensor presents regions with similar colors for similar terms. It reads 103 equals 3 times 2 times 2 times 2 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 103. Similar color means similar terms

Full size image
Fig. 50
A tensor for prime number 107. The tensor presents regions with similar colors for similar terms. It reads 107 equals 6 times 2 plus 1 times 2 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 107. Similar color means similar terms

Full size image
Fig. 51
A tensor for prime number 107. The tensor presents regions with similar colors for similar terms. It reads 107 equals 2 times 3 times 2 plus 1 times 2 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 107. Similar color means similar terms

Full size image
Fig. 52
A tensor for prime number 107. The tensor presents regions with similar colors for similar terms. It reads 107 equals 3 times 2 times 2 plus 1 times 2 times 2 plus 1 times 2 plus 1.

Tensors for prime numbers, 107. Similar color means similar terms

Full size image

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Pramanik, S., Sarkar, J., Singh, P., Ray, K., Bandyopadhyay, A. (2024). Revisiting Self-Operating Mathematical Universe (SOMU) as a Theory for Artificial General Intelligence, AGI and G+ Consciousness. In: Bandyopadhyay, A., Ray, K. (eds) Brain-like Super Intelligence from Bio-electromagnetism. Studies in Rhythm Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-97-0232-9_6

Download citation

Publish with us

Policies and ethics

Search

Navigation