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Tacit Learning – Machine Learning Paradigm Based on the Principles of Biological Learning

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Intelligent Assistive Robots

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 106))

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Abstract

Adaptations to unpredictable environmental changes enable living organisms to survive in their natural environments and are therefore the highest-priority tasks for all of them. In the long history of evolution, living organisms have developed regulatory systems that can adapt their activities to the environment and, as a result have been able to extend their activity fields to almost all places on the earth.

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References

  1. Bear, M.F., Connors, B.W., Paradios, M.A.: Neuroscience: Exploring the brain, 3rd edn. Lippincott Williams and Wilkinse (2006)

    Google Scholar 

  2. Keener, J.: Mathematical Physiology. Springer (1998)

    Google Scholar 

  3. Tanaka, R.J., Kimura, H.: Mathematical classification of regulatory logics for compound environmental changes. Journal of Theoretical Biology 251, 363–379 (2008)

    Article  MathSciNet  Google Scholar 

  4. Janeway, C.A., Travers, P., Walport, M., Shlomchik, M.J.: Immunobiology: The Immune System in Health and Disease, 5th edn. Garland Science, New York (2001)

    Google Scholar 

  5. Shimoda, S., Yoshihara, Y., Fujimoto, K., Yamamoto, T., Maeda, I., Kimura, H.: Stability analysis of tacit learning based on environmental signal accumulation. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (2012)

    Google Scholar 

  6. Hodgkin, A.L., Huxley, A.F.: Currents carried by sodium and potassium ions through the membrane of the giant axon of loligo. Journal of Physiology 116, 449–472 (1952)

    Article  Google Scholar 

  7. Markram, H., Tsodyks, M.: Redistribution of synaptic efficacy between neocortical pyramidal neurons. Nature 382, 807–810 (1996)

    Article  Google Scholar 

  8. Phares, G.A., Antzoulatos, E.G., Baxter, D.A., Byrne, J.H.: Burst-Induced Synaptic Depression and Its Modulation Controbute to Information Transfer at Aplysia Sensorimotor Synapses: Empirical and Computational Analyses. The Journal of Neuroscience (23), 8392–8401 (2003)

    Google Scholar 

  9. Shepherd, G.M.: The synaptic organization of the brain. Oxford University Press (2003)

    Google Scholar 

  10. Jacob, F., Monod, J.: Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology 3, 318–356 (1961)

    Article  Google Scholar 

  11. Abbott, L.F., Varela, J.A., Sen, K., Nelson, S.B.: Synaptic Depression and Control Gain Control. Science (275), 220–224 (1997)

    Google Scholar 

  12. Cook, D.L., Schwindt, P.C., Grande, L.A., Spain, W.L.: Synaptic depression in the localization of sound. Nature 421, 66–70 (2003)

    Article  Google Scholar 

  13. Fortune, E.S., Rose, G.J.: Short-term synaptic plasticity as a temporal filter. Trends in Neurosciences 24(7), 381–385 (2001)

    Article  Google Scholar 

  14. Castro-Alamancos, M.A.: Different temporal processing of sensory inputs in the rat thalamus during quiescent and information processing states in vivo. Journal of Physiology (539), 567–578 (2002)

    Google Scholar 

  15. Eytan, D., Bernner, N., Marom, S.: Selective adaptation in networks of cortical neurons. Journal of Neuroscience (23), 9349–9356 (2003)

    Google Scholar 

  16. Castellucci, V.F., Pinsker, H., Kupfermann, I., Kandel, E.R.: Neuronal mechanisms of habituation and dishabituation of the gill-withdrawal reflex in Aplysia. Science 167, 1745–1748 (1970)

    Article  Google Scholar 

  17. Grade, L.A., Spain, W.J.: Synaptic Depression as a Timing Device. Physiology (20), 201–210 (2005)

    Google Scholar 

  18. Kawato, M., Furukawa, K., Suzuki, R.: A hierarchical network model for motor control and learning of voluntary movement. Biological Cybernetics 57, 169–185 (1987)

    Article  MATH  Google Scholar 

  19. Minsky, M.L., Papert, S.A.: Perceptron. MIT Press (1969)

    Google Scholar 

  20. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(9) (1986)

    Google Scholar 

  21. Kuniyoshi, Y., Yorozu, Y., Suzuki, S., Sangawa, S., Ohmura, Y., Terada, K., Nagakubo, A.: Emergence and development of embodied cognition: A constructivist approach using robots. Progress in Brain Research 164, 425–445 (2007)

    Article  Google Scholar 

  22. Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics 13, 834–846 (1983)

    Article  Google Scholar 

  23. Doya, K.: Reinforcement learning in continuous time and space. Neural Computation 12, 219–245 (2000)

    Article  Google Scholar 

  24. Tedrake, R., Zhang, T.W., Seung, H.S.: Stochastic policy gradient reinforcement learning on a simple 3d biped. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2004)

    Google Scholar 

  25. Holland, J.H.: Adaptation in natural and artificial systems. MIT Press (1992)

    Google Scholar 

  26. Wang, H., Yang, S., Ip, W.H., Wang, D.: Adaptive primal-dual genetic algorithms in dynamic environments. IEEE Transactions on Systems, Man, and Cybernetics Part B 39, 1348–1361 (2009)

    Article  Google Scholar 

  27. Astrom, K.J., Wittenmark, B.: Adaptive control. Addison Wesley (1989)

    Google Scholar 

  28. Slotine, J.E., Li, W.: Applied Nonlinear Control. Prentice Hall (1991)

    Google Scholar 

  29. Brooks, R.A.: A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2, 12–23 (1986)

    Article  Google Scholar 

  30. Brooks, R.A.: New approaches to robotics. Science 253, 1227–1232 (1991)

    Article  Google Scholar 

  31. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on Systems, Man, and Cybernetics SMC-3, 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  32. Juang, J.G.: Fuzzy neural network control CMAC of a biped walking robot. IEEE Transactions on Systems, Man, and Cybernetics Part B 30(4), 594–601 (2000)

    Article  Google Scholar 

  33. Shimoda, S., Kimura, H.: Bio-mimetic Approach to Tacit Learning based on Compound Control. IEEE Transactions on Systems, Man, and Cybernetics-Part B 40(1), 77–90 (2010)

    Article  Google Scholar 

  34. Shimoda, S., Yoshihara, Y., Kimura, H.: Adaptability of tacit learning in bipedal locomotion. IEEE Transactions on Autonomous Mental Development 5(2), 152–161 (2013)

    Article  Google Scholar 

  35. Forssberg, H.: Ontogeny of human locomotor control. I. Infant stepping, supported locomotion and transition to independent locomotion. Exp. Brain Res. 57(3), 480

    Google Scholar 

  36. Shimoda, S., Kimura, H.: Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion. SICE Journal of Control, Measurement, and System Integration 1(4), 275–283 (2008)

    Article  Google Scholar 

  37. McCulloch, W.S., Pitts, W.: A logical calculus of the idea immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  38. Hebb, D.O.: The organization of behavior. Wiley, New York (1949)

    Google Scholar 

  39. http://btcc.nagoya.riken.jp/biologic/movies.html

  40. Sockol, M.D., Raichlen, D.A., Pontze, H.: Chimpanzee locomotor energetics and the origin of human bipedalism. PNAS 104(30), 12265–12269 (2007)

    Article  Google Scholar 

  41. Collins, S., Ruina, A., Tedrake, R., Wisse, M.: Efficient bipedal robots based on passive-dynamic walkers. Science 307, 1082–1085 (2005)

    Article  Google Scholar 

  42. http://workd.honda.com/asimo/

  43. Gibson, J.J.: The ecological approach to visual perception (new edition). Psychology Press (1986)

    Google Scholar 

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Correspondence to Shingo Shimoda .

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Shimoda, S. (2015). Tacit Learning – Machine Learning Paradigm Based on the Principles of Biological Learning. In: Mohammed, S., Moreno, J., Kong, K., Amirat, Y. (eds) Intelligent Assistive Robots. Springer Tracts in Advanced Robotics, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-12922-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-12922-8_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12921-1

  • Online ISBN: 978-3-319-12922-8

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