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
Gorse D and Taylor JG. An analysis of noisy RAM and neural nets. Physica 1989; D34:90–114
Aleksander I. The logic of connectionist systems. In: Aleksander I (ed) Neural Computing Architectures. MIT Press, 1989, pp 133–155
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
Gorse D and Taylor JG. A general model of stochastic neural processing. Biol. Cybem. 1990; 63:299–306
Gorse D and Taylor JG. Universal associative stochastic learning automata. Neural Network World 1991; 1:193–202
Gorse D and Taylor JG. A continuous input RAM-based stochastic neural model. Neural Networks 1991; 4:657–665
Clarkson TG, Gorse D and Taylor JG. From wetware to hardware: reverse engineering using probabilistic RAMs (to appear in Journal of Intelligent Systems)
Barto AG and Anandan P. Pattern recognising stochastic learning automata. IEEE Trans. Syst., Man, Cyb. 1985; SMC-15:360–375
Taylor JG. Spontaneous behaviour in neural networks. J. Theor. Biol. 1972; 36:513–528
Bressloff PC and Taylor JG. Random iterative networks. Phys. Rev. 1990; A41:1126–1137
Amari, SI. Characteristics of random nets of analog neuron-like elements. IEEE Trans. Syst., Man, Cyb. 1972; SMC-2:643–657
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)
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
Gorse D and Taylor JG. Learning sequential structure with recurrent pRAM nets. In: Proceedings of IJCNN Seattle, 1991, pp 37–42.
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
Barto AG and Sutton RS. Landmark learning: an illustration of associative search. Biol. Cybern. 1981;42:1–8
Myers CE. Reinforcement training when results are delayed and interleaved in time. In: Proceedings of INNC-90-Paris, 1990, pp 860–863
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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
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