Abstract
We describe neural network model of unconscious processing as it defined by the symmetrical logic of Matte Blanco. The model system consists of hierarchy of ensembles of Hopfield network representing definite classes of objects. Patterns in each of a network are considered as in some sense identical representatives of given class. These networks generate their self-reproducible descendants which can exchange patterns with each other and generate self-reproducible networks of a higher level representing wider classes of objects. We also give some examples of applications of this model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McLaughlin, J.T.: Primary and secondary process in the context of cerebral hemispheric specialization. Psychoanal. Q. 47, 237–266 (1978)
Stanovich, K.E.: Who is Rational? Studies of Individual Differences in Reasoning. ErlBaum, Mahwah (1999)
Kahneman, D.: Thinking, Fast and Slow. Straus and Giroux, New York (2011)
Sprugnoli, G., et al.: Neural correlates of Eureka moment. Intelligence 62(C), 99–118 (2017)
Belavkin, R.V.: The role of emotion in problem solving. In: Proceedings of the AISB 2001 Symposium on Emotion, Cognition and Affective Computing, England, pp. 49–57 (2001)
Ezhov, A.A., Khrennikov, A.Yu.: Agents with left and right dominant hemispheres and quantum statistics. Phys. Rev. E. 71, 016138:1–016138:8 (2005)
Matte Blanco, I.: The Unconscious as Infinite Sets: An Essay in BiLogic. Karnac Books, London (1975)
Poincare, H.: The Foundations of Science: Science and Hypothesis, the Value of Science, and Science and Method. Science Press, New York (1921)
Gainotti, G.: Unconscious processing of emotions and the right hemisphere. Neuropsychologia 50, 205–218 (2012)
Kühn, S., Ritter, S.M., Müller, B.C.N., van Baaren, R.B., Brass, M., Dijksterhuis, A.: The importance of unconscious processes in creativity – a structural MRI study. J. Creat. Behav. 48(2), 152–163 (2014)
Creswell, J.D., Bursley, J.K., Satpute, A.B.: Neural reactivation links unconscious thought to decision-making performance. Soc. Cogn. Affect. Neurosci. 8, 863–869 (2013)
Evans, J.St.B.T., Stanovich, K.E.: Dual-process theories of higher cognition: advancing the debate. Perspect. Psychol. Sci. 8(3), 223–241 (2013)
Hopfield, J.J.: Neural networks and physical systems with emergent computational abilities. PNAS USA 79, 2554–2558 (1982)
Ezhov, A.A., Vvedensky, V.L., Khromov, A.G., Knizhnikova, L.A.: Self-reproducible neural networks with synchronously changing neuronal threshold. In: Holden, A.V., Kryukov, V.I. (eds.) Neurocomputers and Attention II: Connectionism and Neurocomputers, pp. 523– 534. Manchester University Press (1991)
Ezhov, A.A., Khromov, A.G., Knizhnikova, L.A., Vvedensky, V.L.: Self-reproducible networks: classification, antagonistic rules and generalization. Neural Netw. World 1, 52–57 (1991)
McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 113–115 (1943)
Freud, S.: The unconscious. SE 14, 159–204 (1915)
Behrman, E.C., Gaddam, K., Steck, J.E., Skinner, S.R.: Microtubules as a quantum Hopfield network. In: Tuszynski, J.A. (ed.) The Emerging Physics of Consciousness, pp. 351–370. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-36723-3_10
Srivastava, D.P., Sahni, V., Satsangi, P.S.: Modelling microtubules in the brain as n-qudit quantum Hopfield network and beyond. Int. J. Gen. Syst. 45, 41–54 (2016)
Murtagh, F.: Ultrametric model of mind, II: application to text content analysis. p-Adic Numbers Ultrametric Anal. Appl. 4, 207–221 (2012)
Khrennikov, A.Yu.: Human subconscious as a p-adic dynamical system. J. Theor. Biol. 193, 179–196 (1998)
Lauro-Grotto, R.: The unconscious as an ultrametric set. Am. Imago 64, 535–543 (2008)
Sulehria, H.M., Zhang, Y.: Study on the capacity of Hopfield neural networks. Inf. Technol. J. 7, 684–688 (2008)
Ezhov, A.A., Vvedensky, V.L.: Object generation with neural networks (when spurious memories are useful). Neural Netw. 9, 1491–1495 (1996)
Ezhov, A.A., Kalambet, Ya.A., Knizhnikova, L.A.: Neural networks: general properties and particular applications. In: Holden, A.V., Kryukov, V.I. (eds.) Neural Networks – Theory and Architecture, pp. 39–47. Manchester University Press (1990)
Ezhov, A.A., Berman, G.P.: Introduction to Quantum Neural Technologies. Rinton Press, Princeton (2003)
Fernando, C., Szathmáry, E., Husbands, P.: Selectionist and evolutionary approaches to brain function: acritical appraisal. Front. Comput. Neurosci. 6, 24 (2012)
Kitzbichler, M.G., Smith, M.L., Christensen, S.R., Bullmore, E.: Broadband criticality of human brain network synchronization. PLoS Comput. Biol. 5(3), e1000314 (2009)
Bilotta, E., Lafusa, A., Pantano, P.: Is self-replication an embedded characteristic of artificial/living matter? In: Artificial Life VIII: Proceedings of the Eighth International Conference on the Simulation and Synthesis of Living Systems (2002)
James, A., Pierowicz, J., Moskal, M., Hanratty, T., Tuttle, D., Sensenig, B., Hedges, B.: Assessing consequential scenarios in a complex operational environment using agent-based simulation. ARL-TR-7954 (2017)
Tsien, J.: The memory code. Sci. Am. 297, 52–59 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Ezhov, A.A. (2018). Neural Network Model of Unconscious. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-92537-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92536-3
Online ISBN: 978-3-319-92537-0
eBook Packages: Computer ScienceComputer Science (R0)