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Dynamics of a Classical Conditioning Model

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ICANN 98 (ICANN 1998)

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

In this paper, the model of classical conditioning introduced by Balkenius [1, 2, 3] is analyzed with varying sequential stimulus configurations. We show that the learning converges under arbitrary stable stimulus sequences and further investigate how the asymptotic CR amplitude depends on CS and US magnitude and timing.

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References

  1. Balkenius C. Natural intelligence in artificial creatures. PhD thesis, Lund University Cognitive Studies 37, 1995

    Google Scholar 

  2. Balkenius C. Generalization in instrumental learning. In Maes P, Mataric M, Meyer J.-A, Pollack J, Wilson S W. (eds) From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press/Bradford Books, Cambridge, MA, 1996

    Google Scholar 

  3. Balkenius C. A neural network model of classical conditioning I: the dynamics of learning, Lund University Cognitive Studies 68, 1998

    Google Scholar 

  4. Hull C.L. The goal-gradient hypothesis and maze learning. Psychological Review 1932, 39, 1, 25–43

    Article  Google Scholar 

  5. Watkins C J C H. Q-learning. Machine Learning 1992, 8, 279–292

    MATH  Google Scholar 

  6. Desmond J E. Temporally adaptive responses in neural models: the stimulus trace. In: Gabriel M, Moore J. (eds) Learning and computational neuroscience: foundations of adaptive networks. MIT Press, Cambridge, MA, 1990, pp 421–456

    Google Scholar 

  7. Grossberg S. The adaptive brain. North-Holland, Amsterdam, 1987

    Google Scholar 

  8. Gray J A. Elements of a two-process theory of learning. Academic Press, London, 1975

    Google Scholar 

  9. Balkenius C, Morén J. Computational models of classical conditioning: a comparative study. In: From animals to animats 5. MIT Press, Cambridge, MA, 1998

    Google Scholar 

  10. Klopf A H. A neuronal model of classical conditioning. Psychobiology 1988, 16, 2, 85–125

    Google Scholar 

  11. Sutton R S, Barto A G. Time-derivative models of Pavlovian reinforcement. In: Gabriel M, Moore J. (eds) Learning and computational neuroscience: foundations of adaptive networks. MIT Press, Cambridge, MA, 1990, pp. 497–538

    Google Scholar 

  12. Rescorla R A, Wagner A R. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Black A H, Prokasy W F. (eds) Classical conditioning H: current research and theory. Appleton-Century-Crofts, New York, 1972, pp. 64–99

    Google Scholar 

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© 1998 Springer-Verlag London

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Balkenius, C., Morén, J. (1998). Dynamics of a Classical Conditioning Model. In: Niklasson, L., Bodén, M., Ziemke, T. (eds) ICANN 98. ICANN 1998. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1599-1_65

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  • DOI: https://doi.org/10.1007/978-1-4471-1599-1_65

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76263-8

  • Online ISBN: 978-1-4471-1599-1

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