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Electronic Equivalent of Consciousness with Elementary Mental Process Model

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

We present a project to design of the simplified model of thinking located in a little-explored field between neurobiology and psychology. While neurobiology in the incredibly complex microscopic system is dedicated to the structures and signals at the molecular level and the psychology is committed at the opposite extreme with highly sophisticated, they are very abstract and, therefore, difficult to grasp these wholes. There is a vast space between these two extremes. From the perspective of the inherent observers, it is investigable without overcoming the complexity of both points of interest. The primary goal of this research is to construct a multi-layer analog configuration space, the Electronic Equivalent of Consciousness (ECC), wherein the signals have the same properties (bioelectrical) as in the human brain.

Faculty of Electrical Engeneering, Czech Technical University in Prague, Czechia.

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References

  1. Almási, A.D., Woźniak, S., Cristea, V., Leblebici, Y., Engbersen, T.: Review of advances in neural networks: neural design technology stack. Neurocomputing 174, 31–41 (2016)

    Article  Google Scholar 

  2. Aylett, M., Turk, A.: The smooth signal redundancy hypothesis: a functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Lang. Speech 47(1), 31–56 (2004)

    Article  Google Scholar 

  3. Bahrick, L., Lickliter, R.: Intersensory redundancy guides attentional selectivity and perceptual learning in infancy. Dev. Psychol. 36(2), 190–201 (2000). https://doi.org/10.1037//0012-1649.36.2.190

    Article  Google Scholar 

  4. Bahrick, L., Lickliter, R.: Intersensory redundancy guides early perceptual and cognitive development. In: Kail, R.V. (ed.) Advances in Child Development and Behavior, vol. 30, pp. 153–187. Elsevier, Boston (2002)

    Google Scholar 

  5. Bahrick, L.E., Lickliter, R., Castellanos, I., Todd, J.T.: Intrasensory redundancy facilitates infant detection of tempo: extending predictions of the intersensory redundancy hypothesis. Infancy 20(4), 377–404 (2015). https://doi.org/10.1111/infa.12081. https://onlinelibrary.wiley.com/doi/abs/10.1111/infa.12081

    Article  Google Scholar 

  6. Benka, T., Havranek, M., Hejtmanek, M., Jakovenko, J., Janoska, Z., Marcisovska, M., Marcisovsky, M., Neue, G., Tomasek, L., Vrba, V.: Characterization of pixel sensor designed in 180 nm SOI CMOS technology. J. Instrum. 13(1), C01025–C01025 (2018)

    Article  Google Scholar 

  7. Cabibihan, J., Joshi, D., Srinivasa, Y.M., Chan, M.A., Muruganantham, A.: Illusory sense of human touch from a warm and soft artificial hand. IEEE Trans. Neural Syst. Rehabil. Eng. 23(3), 517–527 (2015). https://doi.org/10.1109/TNSRE.2014.2360533

    Article  Google Scholar 

  8. Chandra, B., Sharma, R.K.: Deep learning with adaptive learning rate using laplacian score. Expert Syst. Appl. 63, 1–7 (2016). https://doi.org/10.1016/j.eswa.2016.05.022. http://www.sciencedirect.com/science/article/pii/S0957417416302470

    Article  Google Scholar 

  9. Crochet, S., Lee, S.H., Petersen, C.C.: Neural circuits for goal-directed sensorimotor transformations. Trends Neurosci. 42(1), 66–77 (2019). https://doi.org/10.1016/j.tins.2018.08.011. http://www.sciencedirect.com/science/article/pii/S0166223618302364

    Article  Google Scholar 

  10. Dovhij, V., Holota, V., Kogut, I.: Architecture development and elements simulation of analytical microsystem-on-chip with “silicon-on-insulator” structures. In: 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pp. 368–372 (2016)

    Google Scholar 

  11. Drukarch, B., Holland, H.A., Velichkov, M., Geurts, J.J.G., Voorn, P., Glas, G., de Regt, H.W.: Thinking about the nerve impulse: a critical analysis of the electricity-centered conception of nerve excitability. Prog. Neurobiol. 169, 172–185 (2018)

    Article  Google Scholar 

  12. Duan, S., Hu, X., Dong, Z., Wang, L., Mazumder, P.: Memristor-based cellular nonlinear/neural network: design, analysis, and applications. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1202–1213 (2014)

    Article  MathSciNet  Google Scholar 

  13. Futagi, Y.: Eye-hand-mouth coordination in the human newborn. Pediatr. Neurol. 75, 43–47 (2017)

    Article  Google Scholar 

  14. Golovin, M.S., Balioz, N.V., Krivoschekov, S.G., Aizman, R.I.: Integration of functional, psychophysiological, and biochemical processes in athletes after audiovisual stimulation. Hum. Physiol. 44(1), 54–59 (2018)

    Article  Google Scholar 

  15. Graham, S.A., Depp, C.A.: Artificial intelligence and risk prediction in geriatric mental health: what happens next? Int. Psychogeriatr. 31(7), 921–923 (2019). https://doi.org/10.1017/S1041610219000954

    Article  Google Scholar 

  16. Helmbrecht, T.O., dal Maschio, M., Donovan, J.C., Koutsouli, S., Baier, H.: Topography of a visuomotor transformation. Neuron 100(6), 1429–1445.e4 (2018)

    Article  Google Scholar 

  17. Hickey, C., Peelen, M.: Neural mechanisms of incentive salience in naturalistic human vision. Neuron 85(3), 512–518 (2015). https://doi.org/10.1016/j.neuron.2014.12.049. http://www.sciencedirect.com/science/article/pii/S0896627314011581

    Article  Google Scholar 

  18. Hitier, M., Sato, G., Zhang, Y.F., Zheng, Y., Besnard, S., Smith, P.F., Curthoys, I.S.: Anatomy and surgical approach of rat’s vestibular sensors and nerves. J. Neurosci. Methods 270, 1–8 (2016)

    Article  Google Scholar 

  19. Holland, S.: Artificial intelligence, education and music: the use of artificial intelligence to encourage and facilitate music composition by novices (1989)

    Google Scholar 

  20. Huang, M.H., Rust, R., Maksimovic, V.: The feeling economy: managing in the next generation of artificial intelligence (AI). Calif. Manag. Rev. 61(4), 43–65 (2019). https://doi.org/10.1177/0008125619863436

    Article  Google Scholar 

  21. Huda, R., Goard, M.J., Pho, G.N., Sur, M.: Neural mechanisms of sensorimotor transformation and action selection. Eur. J. Neurosci. 49(8), 1055–1060 (2019)

    Article  Google Scholar 

  22. Iurlaro, M., von Meyenn, F., Reik, W.: Dna methylation homeostasis in human and mouse development. Curr. Opin. Genet. Dev. 43, 101–109 (2017)

    Article  Google Scholar 

  23. Jacobs, L.F.: Of space and smell: the strange evolution of the human nose. In: HRI 2017: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 350–351. Association for Computing Machinery, New York (2017 )

    Google Scholar 

  24. Jang, S.H., Kwon, H.G.: The ascending reticular activating system from pontine reticular formation to the hypothalamus in the human brain: a diffusion tensor imaging study. Neurosci. Lett. 590, 58–61 (2015)

    Article  Google Scholar 

  25. Ji, M., Su, X., Su, X., Chen, Y., Huang, W., Zhang, J., Gao, Z., Li, C., Lu, X.: Identification of novel compounds for human bitter taste receptors. Chem. Biol. Drug Des. 84(1), 63–74 (2014)

    Article  Google Scholar 

  26. Lazovic, B., Zlatkovic Svenda, M., Durmic, T., Stajic, Z., Duric, V., Zugic, V.: The regulation role of carotid body peripheral chemoreceptors in physiological and pathophysiological conditions. Med. pregl. 69(11–12), 385–390 (2016)

    Article  Google Scholar 

  27. Lima, P.M., Ford, N.J., Lumb, P.M.: Computational methods for a mathematical model of propagation of nerve impulses in myelinated axons. Appl. Numer. Math. 85, 38–53 (2014)

    Article  MathSciNet  Google Scholar 

  28. Liston, A., Carr, E., Linterman, M.: Series: lifetime immunity shaping variation in the human immune system. Trends Immunol. 37(10), 637–646 (2016)

    Article  Google Scholar 

  29. Manson, G., Blouin, J., Kumawat, A., Crainic, V., Tremblay, L.: Rapid online corrections for upper limb reaches to perturbed somatosensory targets: evidence for non-visual sensorimotor transformation processes. Exp. Brain Res. 237(3), 839–853 (2019)

    Article  Google Scholar 

  30. Mayrhofer, J.M., El-Boustani, S., Foustoukos, G., Auffret, M., Tamura, K., Petersen, C.C.H.: Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron 103(6), 1034–1043.e5 (2019)

    Article  Google Scholar 

  31. McCarthy, J.: What has ai in common with philosophy? In: IJCAI, pp. 2041–2044 (1995)

    Google Scholar 

  32. Orioli, G., Bremner, A.J., Farroni, T.: Multisensory perception of looming and receding objects in human newborns. Curr. Biol. 28(22), R1294–R1295 (2018)

    Article  Google Scholar 

  33. Pham, H.L.: Characterisations of von neumann algebras. J. Math. Anal. Appl. 454(2), 542–556 (2017)

    Article  MathSciNet  Google Scholar 

  34. Saini, S., Singh, P.: Von neumann stability of modified loop quantum cosmologies. Class. Quantum Gravity 36(10), 105010 (2019)

    Article  MathSciNet  Google Scholar 

  35. Sebastian, A., Le Gallo, M., Eleftheriou, E.: Computational phase-change memory: beyond von neumann computing. J. Phys. D-Appl. Phys. 52(44), 443002 (2019)

    Article  Google Scholar 

  36. Sood, S.O.: Emotional computation in artificial intelligence education. In: AAAI (2008)

    Google Scholar 

  37. Sottek, R., Genuit, K.: Models of signal processing in human hearing. AEU - Int. J. Electron. Commun. 59(3), 157–165 (2005). https://doi.org/10.1016/j.aeue.2005.03.016. http://www.sciencedirect.com/science/article/pii/S1434841105000701

    Article  Google Scholar 

  38. Sterne, J., Razlogova, E.: Machine learning in context, or learning from LANDR: artificial intelligence and the platformization of music masterin. Soc. Media + Soc. 5(2), 2056305119847525 (2019). https://doi.org/10.1177/2056305119847525

    Article  Google Scholar 

  39. Tuthill, J.C., Azim, E.: Proprioception. Curr. Biol. 28(5), R194–R203 (2018). https://doi.org/10.1016/j.cub.2018.01.064. http://www.sciencedirect.com/science/article/pii/S0960982218300976

    Article  Google Scholar 

  40. Wu, W., Guo, Z., Zhou, X., Wu, H., Zhang, X., Lian, R., Wang, H.: Proactive human-machine conversation with explicit conversation goals (2019)

    Google Scholar 

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Acknowledgments

Research described in the paper was supported by the Czech Technical University grant SGS20/176/OHK3/3T/133 and Nadace Science 21 foundation.

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Authors and Affiliations

  1. FEE in Prague, Czech Technical University in Prague, Prague, Czechia

    Leonard Bernau, Filip Paulu & Jan Voves

Authors
  1. Leonard Bernau
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  2. Filip Paulu
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  3. Jan Voves
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Corresponding author

Correspondence to Leonard Bernau .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Bernau, L., Paulu, F., Voves, J. (2020). Electronic Equivalent of Consciousness with Elementary Mental Process Model. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_38

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_38

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