Abstract
In the 1920s, Brodmann found that the neurons arrange in around 47 distinct patterns in the brain’s topmost thin cortex layer. Each region controls a distinct brain function. Together, the cortex is made of 120,000–200,000 cortical columns, executing all cognitive responses. By filling capillary glass tubes with helical carbon nanotube, we built a corticomorphic device as a replacement of the neuromorphic device and using 10,000 such corticomorphic devices built a cortex replica. Using dielectric and cavity resonators, we built a complex nerve fiber network of the entire brain–body system. It includes connectome, spinal cord, and similar ten major organs. The nerve fiber network takes input from wide ranges of sensors, and the neural paths interact before changing the self-assembly of helical carbon nanotubes, which is read using EEG or laser refraction. The integrated brain–body system is our humanoid bot subject, HBS. One could refill entire cortex region with new synthetic organic materials to test spontaneous, software-free 24 × 7 brain response in EEG and optical vortices. Our extensive theoretical simulations of all brain components were verified with hardware replicas in the optoelectronic HBS. HBS is a universal tool to test a brain hypothesis using AI chips, organic–inorganic materials, etc.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lancaster, M.A., et al.: Cerebral organoids model human brain development and microcephaly. Nature 501(7467), 373–379 (2013)
Lancaster, M.A., et al.: Guided self-organization and cortical plate formation in human brain organoids. Nat. Biotechnol. 35(7), 659–666 (2017)
Tan, Z., Parisi, C., Silvio, L.D., Dini, D., Forte, A.E.: Cryogenic 3D printing of super soft hydrogels. Sci. Rep. 7, 16293 (2017)
Tallinen, T., Chung, J.Y., Rousseau, F., Girard, N., Lefèvre, J., Mahadevan, L.: On the growth and form of cortical convolutions. Nat. Phys. 12, 588–593 (2016)
Bogdan, P.A., Rowley, A.G.D., Rhodes, O., Furber, S.B.: Structural plasticity on the SpiNNaker many-core neuromorphic system. Front. Neurosci. 12, 434 (2018)
Bressler, S.L., Kelso, J.A.S.: Cortical coordination dynamics and cognition. Trends Cogn. Neurosci. 5, 26–36 (2001)
Chklovskii, D.B., Mel, B.W., Svoboda, K.: Cortical rewiring and information storage. Nature 431, 782–788 (2007)
Chklovskii, D.B., Schikorski, T., Stevens, C.F.: Wiring optimization in cortical circuits. Neuron 34, 341–347 (2002)
Compte, A., Sanchez-Vives, M.V., McCormick, D.A., Wang, X.J.: Cellular and network mechanisms of slow oscillatory activity (<1 Hz) and wave propagations in a cortical network model. J. Neurophysiol. 89, 2707–2725 (2003)
Dupont, E., Hanganu, I.L., Kilb, W., Hirsch, S., Luhmann, H.J.: Rapid developmental switch in the mechanisms driving early cortical columnar networks. Nature 439, 79–83 (2006)
Eckhorn, R.: Cortical processing by fast synchronization: high frequency rhythmic and non-rhythmic signals in the visual cortex point to general principles of spatiotemporal coding: time and the brain. In: Miller, R. (ed.), pp. 169–201. Harwood Academic Publishers (2000)
Phillips, W.A., Singer, W.: In search of common foundations for cortical computation. Behav. Brain Sci. 20, 657–683 (1997)
We Almost Gave Up On Building Artificial Brains. https://www.discovermagazine.com/technology/we-almost-gave-up-on-building-artificial-brains
Human Brain Project. https://www.humanbrainproject.eu/en/
SpiNNaker Project. http://apt.cs.manchester.ac.uk/projects/SpiNNaker/project/
DeepMind’s Losses and the Future of Artificial Intelligence. https://www.wired.com/story/deepminds-losses-future-artificial-intelligence/
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: Soft Computing Application. Springer, Singapore (2018)
Agrawal, L., et al.: Replacing Turing tape with a fractal tape: a new information theory, associated mechanics and decision making without computing. In: Consciousness: Integrating Indian and Western perspective, pp. 87–159 (2016)
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: Lecture Notes in Electrical Engineering, vol. 478, pp. 717–733 (2018)
Singh, P., et al.: 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)
Yamins, D.L.K., DiCarlo, J.J.: Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016)
Wheeler, J.A.: On the nature of quantum geometrodynamics. Ann. Phys. 2, 604–614 (1957)
Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yucel, M., et al.: Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50, 970–983 (2010)
Hart, Y., Dillon, M.R., Marantan, A., Cardenas, A.L., Spelke, E., Mahadevan, L.: The statistical shape of geometric reasoning. Sci. Rep. 8(12906), 1–12 (2018)
Terekhovich, V.E.: Probabilistic and geometric languages in the context of the principle of least action. Philos. Sci. Novosibirsk 52(2), 108–120 (2012)
Sweet, H.: Universal languages. In: Encyclopædia Britannica, 11th edn (1911). https://en.wikipedia.org/wiki/Universal_language
Singh, P., et al.: A space-time-topology-prime, stTS metric for a self-operating mathematical universe uses dodecanion geometric algebra of 1-20D complex vectors. In: LNSS, Lecture notes on networks and signals. Springer Nature, Berlin, in press (2019)
Singh, P., et al.: Quaternion octonion to dodecanion manifold: stereographic projections from infinity lead to a self-operating mathematical universe. In: AISC, Advanced Intelligence Systems and Computing. Springer Nature, Berlin, in press (2019)
Bandyopadhyay, A.: Chapter 2 FIT, and GML; Chapter 3 PPM; Chapter 7. A complete, integrated time crystal model of a human brain. In: Nanobrain. The Making of an Artificial Brain from a Time Crystal, 372 p. Taylor & Francis Inc. Imprint CRC Press Inc., Bosa Roca (2020)
Amalric, M., Wang, L., Pica. P., Figueria, S., Sigman, M., Dehaene, S.: The language of geometry: fast comprehension of geometrical primitives and rules in human adults and preschoolers. PLoS Comput. Biol. 13(1), e1005273 (2017)
Dehaene, S., Izard, V., Pica, P., Spelke, E.: Core knowledge of geometry in an Amazonian Indigene Group. Science 311(5759), 381–384 (2006)
Guevara, M.R., Jongsma, H.J.: Phase resetting in a model of sinoatrial nodal membrane: ionic and topological aspects. Am. J. Physiol. 258, 734–747 (1990)
Agrawal, L., et al.: Fractal information theory (FIT) derived geometric musical language (GML) for brain inspired hypercomputing. In: Advances in Intelligent Systems and Soft Computing (AISC), vol. 2, pp. 37–61. Springer, Berlin (2016)
Sotiropoulos, S.N., et al.: Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80, 125–143 (2013)
Buzsáki, G.: The rhythms of the brain. Oxford University Press, London (2006). https://doi.org/10.1093/acprof:oso/9780195301069.001
Ramkumar, P., Parkkonen, L., Hari, R., Hyvärinen, A.: Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis. Hum. Brain Mapp. 33(7), 1648–1662 (2012)
Ghosh, S., Sahu, S., Fujita, D., Bandyopadhyay, A.: 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 (2014)
Carter, R.: The human brain book: an illustrated guide to its structure, function, and disorders. DK, London (2014)
Liu, Z., Fang, N., Yen, T.J., Zang, X.: Rapid growth of evanescent wave by a silver superlens. Appl. Phys. Lett. 83, 5184–5187 (2003)
Wiltshire, M.C.K., et al.: Metamaterial endoscope for magnetic field transfer: near field imaging with magnetic wires. Opt. Express 11(7), 709–715 (2003)
Milosevic, M.: On the nature of the evanescent wave. Appl. Spectrosc. 26(2), 126–131 (2013)
Plebe, A., Domenella, R.G.: Neural networks; Object recognition by artificial cortical maps. Neural Networks 20(7), 763–780 (2007)
Striegel, D.A., Hurdal, M.K.: Chemically based mathematical model for development of cerebral cortical folding patterns. PLoS Comput. Biol. 5(9), e1000524 (2019)
Reimann, M.W., King, J.G., Muller, E.B., Ramaswamy, S., Markram, H.: An algorithm to predict the connectome of neural microcircuits. Front. Comput. Neurosci. 9, 1–8 (2015)
Braitenberg, V., Braitenberg, C.: Geometry of orientation columns in the visual cortex. Biol. Cybern. 33, 179–186 (1979)
Klyachko, V.A., Stevens, C.F.: Connectivity optimization and the positioning of cortical areas. Proc. Natl. Acad. Sci. 100, 7937–7941 (2003)
Mountcastle, V.B.: The columnar organization of the neocortex. Brain 120, 701–722 (1997)
Gevins, A., Smith, M.E., McEvoy, L., Yu, D.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7, 374–485 (1997)
Harris, K.D., Barthó, P., Zugaro, M.B., Monconduit, L., Marguet, S., Buzsaki, G.: Neocortical population patterns during EEG activation: waking, REM, and anesthetised states. Society for Neuroscience Meeting, Washington DC, abstract.719.5 (2003)
Brodmann, K.: Vergleichende Lokalisationslehre der Grosshirnrinde. Johann Ambrosius Barth, Leipzig (1909)
Garey, L.J.: Brodmann’s: localisation in the cerebral cortex. Springer, New York (2006)
CST Microwave Studio. https://perso.telecom-paristech.fr/begaud/intra/MWS_Tutorials.pdf
Markram, H., Muller, E., Ramaswamy, S., Reimann, M.W., Abdellah, M., Sanchez, C.A., et al.: Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015)
Horton, J.C., Adams, D.L.: The cortical column: a structure without a function. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1456), 837–862 (2005)
Haueis, P.: The life of the cortical column: opening the domain of functional architecture of the cortex (1955–1981). Hist. Philos. Life Sci. 38(2) (2016)
Ghosh, S., et al.: Inventing a co-axial atomic resolution patch clamp to study a single resonating protein complex and ultra-low power communication deep inside a living neuron cell. J. Integr. Neurosci. 15(4), 403–433 (2016)
Venkataraman, A., Amadi, E.V., Chen, Y., Papadopoulos, C.: Carbon nanotube assembly and integration for applications. Nanoscale Res. Lett. 14(1), 220 (2019)
Singh, P., et al.: Fractal and periodical biological antennas: hidden topologies in DNA, wasps and retina in the eye. In: Soft Computing Applications, pp. 113–130. Springer, Singapore (2018)
Singer, W.: Synchronization of cortical activity and its putative role in information processing and learning. Annu. Rev. Physiol. 55, 349–374 (1993)
Shi, J., Thompson, P.M., Wang, Y.: Human brain mapping with conformal geometry and multivariate tensor-based morphometry. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds) Multimodal Brain Image Analysis, MBIA. Lecture Notes in Computer Science. Springer, Berlin (2011)
Muller, A.A., Soto, P., Dascalu, D., Neculoiu, D., Boria, V.E.: A 3-D smith chart based on the Riemann sphere for active and passive microwave circuits. IEEE Microwave Wirel. Compon. Lett. 21(6), 286–288 (2011)
Muller, A.A., Dascalu, D.C., Pacheco, P.S., Esbert, V.E.B.: The 3D Smith chart and its practical applications. Microw. J. 55(6), 64–74 (2012)
Gawne, T.J., Richmond, B.J.: How independent are the messages carried by adjacent inferior temporal cortical neurons? J. Neurosci. 13, 2758–2771 (1993)
Zeki, S., Shipp, S.: The functional logic of cortical connections. Nature 335, 311–317 (1988)
Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.: A resilient, low frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26, 63–72 (2006)
Ahissar, E., Arieli, A.: Figuring space by time. Neuron 32, 185–201 (2001)
Fellous, J.M., Houweling, A.R., Modi, R.H., Rao, R.P., Tiesinga, P.H., Sejnowski, F.J.: Frequency dependence of spiking timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol. 85, 1782–1787 (2001)
Llinás, R.R., Ribary, U., Joliot, M., Wang, X.J.: Content and context in temporal thalamocortical binding. In: Buzsáki, G., Llinás. R., Singer, W., Berthoz, A., Christen, Y. (eds.) Temporal Coding in the Brain, pp. 251–272. Springer, Berlin (1994)
Ikegaya, Y., Aaron, G., Cossart, R., Aronov, D., Lampl, I., Ferster, D., Yester, R.: Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004)
Lopes da Silva, F.H., Storm van Leeuwen, W.: The cortical alpha rhythm of the dog: the depth and profile of phase: architectonics of the cerebral cortex. In: Brazier, M.A.B., Petsche, H. (eds.), pp. 150–187. New York (1978)
Van Essen, D.: A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385, 313–318 (1997)
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 (1994)
Matthews, P.C., Strogatz, S.H.: Phase diagram for the collective behavior of limit-cycle oscillators. Phys. Rev. Lett. 64, 1701–1704 (1990)
Wang, X.J.: Multiple dynamical modes of thalamic relay neurons: rhythmic bursting and intermittent phase-locking. Neuroscience 59, 21–31 (1994)
Graybiel, A.M.: The basal ganglia: learning new tricks and loving it. Curr. Opin. Neurobiol. 15, 638–644 (2005)
Paré, D., Shink, E., Gaudreau, H., Destexhe, A., Lang, E.J.: Impact of spontaneous synaptic activity on the resting properties of cat neocortical pyramidal neurons in vivo. J. Neurophysiol. 79, 1450–1460 (1998)
Shadlen, M.N., Newsome, W.T.: Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994)
Colom, R., et al.: Human intelligence and brain network. Dialogues Clin. Neurosci. 12(4), 489–501 (2010)
Nie, Y., Fellous, J.M., Tatsuno, M.: Information-geometric measures estimate neural interactions during oscillatory brain states. J. Front. Neural Circ. 24, 8–11 (2014)
Saxena, K., et al.: Fractal, scale free electromagnetic resonance of a single brain extracted microtubule nanowire, a single tubulin protein and a single neuron. Fractal Fractional 4(2), 11 (2020)
Friston, K.: The free energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127–138 (2010)
Winfree, A.: Biological Rhythm Research: The Geometry of Biological Time. Springer, New York (1977, 2001)
Miller, K.D., Pinto, D.J., Simons, D.J.: Processing in layer 4 of the neocortical circuit: new insights from visual and somatosensory cortex. Curr. Opin. Neurobiol. 11, 488–497 (2001)
Zeki, S.M.: Uniformity and diversity of structure and function in rhesus monkey prestriate cortex. J. Physiol. 277, 272–290 (1978)
Abeles, M.: Local cortical circuits: studies in brain function. Springer, Berlin (1982)
Sadeh, S., Rotter, S.: Statistics and geometry of orientation selectivity in primary visual cortex. Biol. Cybern. 108(5), 631–653 (2014)
Niu, H., Wang, J., Zhao, T., Shu, N., He, Y.: Correction: revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy. PLoS ONE 7(9), e45771 (2013)
Scannell, J.W., Blakemore, C., Young, M.P.: Analysis of connectivity in the cat cerebral cortex. J. Neurosci. 15(2), 1463–1483 (1995)
Krupa, D.J., Wiest, M.C., Shuler, M.G., Laubach, M., Nicolelis, M.A.: Layer-specific somatosensory cortical activation during active tactile discrimination. Science 304, 1989–1992 (2004)
Reich, D.S., Mechler, F., Victor, J.D.: Independent and redundant information in nearby cortical neurons. Science 294, 2566–2568 (2001)
Katz, L.C., Shatz, C.J.: Synaptic activity and the construction of cortical circuits. Science 274, 1133–1138 (1996)
Schütz, A., Braitenberg, V.: The human cortical white matter: quantitative aspects of cortico-cortical long-range connectivity. In: Schütz, A., Miller, R. (eds.) Cortical Areas: Unity and Diversity. Taylor and Francis, Milton Park (2002)
Shu, Y., Hasenstaub, A., McCormick, D.: Turning on, off recurrent balanced cortical activity. Nature 423, 288–293 (2003)
Acknowledgements
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 the electromagnetic resonance-based communication and intelligence of biomaterials.
Competing Interests
The authors declare that there is no competing interest.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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: 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. Springer, Singapore. https://doi.org/10.1007/978-981-33-4673-4_21
Download citation
DOI: https://doi.org/10.1007/978-981-33-4673-4_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4672-7
Online ISBN: 978-981-33-4673-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)