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Neural Network Dynamics pp 19–29Cite as

Associative Reinforcement Training Using Probabilistic RAM Nets

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

Abstract

It is described how probabilistic RAMs may be applied to problems of associative search, using local reinforcement rules which utilise synaptic rather than threshold noise in the stochastic search procedure. Examples are given of syntactical and spatial learning tasks which successfully use these techniques.

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References

  1. Gorse D and Taylor JG. An analysis of noisy RAM and neural nets. Physica 1989; D34:90–114

    MathSciNet  Google Scholar 

  2. Aleksander I. The logic of connectionist systems. In: Aleksander I (ed) Neural Computing Architectures. MIT Press, 1989, pp 133–155

    Google Scholar 

  3. Clarkson TG, Gorse D and Taylor JG. Hardware realisable models of neural processing. In: Proceedings of the First IEE International Conference on Artificial Neural Networks, 1989, pp 310–314

    Google Scholar 

  4. Gorse D and Taylor JG. A general model of stochastic neural processing. Biol. Cybem. 1990; 63:299–306

    Article  MATH  MathSciNet  Google Scholar 

  5. Gorse D and Taylor JG. Universal associative stochastic learning automata. Neural Network World 1991; 1:193–202

    Google Scholar 

  6. Gorse D and Taylor JG. A continuous input RAM-based stochastic neural model. Neural Networks 1991; 4:657–665

    Article  Google Scholar 

  7. Clarkson TG, Gorse D and Taylor JG. From wetware to hardware: reverse engineering using probabilistic RAMs (to appear in Journal of Intelligent Systems)

    Google Scholar 

  8. Barto AG and Anandan P. Pattern recognising stochastic learning automata. IEEE Trans. Syst., Man, Cyb. 1985; SMC-15:360–375

    MathSciNet  Google Scholar 

  9. Taylor JG. Spontaneous behaviour in neural networks. J. Theor. Biol. 1972; 36:513–528

    Article  Google Scholar 

  10. Bressloff PC and Taylor JG. Random iterative networks. Phys. Rev. 1990; A41:1126–1137

    MathSciNet  Google Scholar 

  11. Amari, SI. Characteristics of random nets of analog neuron-like elements. IEEE Trans. Syst., Man, Cyb. 1972; SMC-2:643–657

    Google Scholar 

  12. Servan-Schreiber D, Cleeremans A and McClelland JL. Encoding sequential structure in simple recurrent networks (paper presented at IEEE Conference on Neural Information Processing Systems, Denver, Colorado, 1988)

    Google Scholar 

  13. Giles CL, Sun GZ, Chen HH, Lee YC and Chen D. Higher order recurrent networks and grammatical inference. In: Touretzky DS (ed) Advances in Neural Information Processing Systems, vol 2. Morgan Kauffman, San Mateo Ca., 1990, pp 380–387

    Google Scholar 

  14. Gorse D and Taylor JG. Learning sequential structure with recurrent pRAM nets. In: Proceedings of IJCNN Seattle, 1991, pp 37–42.

    Google Scholar 

  15. Barto AG, Sutton RS and Anderson CW. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst., Man, Cyb. 1983; SMC-13:834–846

    Google Scholar 

  16. Barto AG and Sutton RS. Landmark learning: an illustration of associative search. Biol. Cybern. 1981;42:1–8

    Article  MATH  Google Scholar 

  17. Myers CE. Reinforcement training when results are delayed and interleaved in time. In: Proceedings of INNC-90-Paris, 1990, pp 860–863

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University College, London, UK

    Denise Gorse

Authors
  1. Denise Gorse
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Editor information

Editors and Affiliations

  1. Centre for Neural Networks, Department of Mathematics, King’s College, Strand, London, WC2R 2LS, UK

    J. G. Taylor BA, BSc, MA, PhD, FlnstP (Director) (Director)

  2. Dipartimento di Fisica Teorica, Università di Salerno, Via S. Allende, 84081, Baronissi (SA), Italy

    E. R. Caianiello

  3. Department of Biophysics, The Technical University of Denmark, Building 307, DK-2800, Lyngby, Denmark

    R. M. J. Cotterill BSc, MS, PhD, DSc, FlnstP, C.Phys.

  4. Department of Physics, Washington University, St Louis, Missouri, 63130, USA

    J. W. Clark

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© 1992 Springer-Verlag London Limited

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Gorse, D. (1992). Associative Reinforcement Training Using Probabilistic RAM Nets. In: Taylor, J.G., Caianiello, E.R., Cotterill, R.M.J., Clark, J.W. (eds) Neural Network Dynamics. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2001-8_2

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  • DOI: https://doi.org/10.1007/978-1-4471-2001-8_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19771-3

  • Online ISBN: 978-1-4471-2001-8

  • eBook Packages: Springer Book Archive

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