Abstract
In this paper we present a neural paradigm for controlling the reflex behaviour of autonomous systems which are able to modify their behaviour by interaction with the environment. This paradigm incorporates the ideas expressed by Russell [1] about how to model the living being's reflex behaviour. In this paradigm a new type of connection is introduced: the so called high order Or connection. Learning is local and unsupervised, i.e., the change in the weight of a connection takes place as a consequence of its activation. We present two functions to update the weights which incorporate the forgetting capability. Some topologies have been simulated to provide the basic capabilities such as inhibition, stimuli association an reinforcement.
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References
S. B. Russell, “A practical device to simulate the working of nervous discharges”, Journal of Animal Behaviour, 3, (15) (1913), pp. 15–35.
R.P. Lippmann, “An Introduction to computing with neural nets”, IEEE ASSP Magazine, April 1987, pp. 4–22.
C.L. Giles and T. Maxwell, “Learning, invariance, and generalization in high-order neural networks”. Applied Optics, 26, (23), (1987), pp. 4972–4978.
M.L. Minsky and S. Papert, Perceptrons, MIT Press, Cambrige, MA, 1969.
J. Mira, A.E. Delgado, J.R. Alvarez, A.P. Madrid, and M. Santos, “Towards more realistic self contained models of neurons: high-order, recurrence and local learning”, in J. Mira, J. Cabestany, A. Prieto eds, New Trends in Neural Computation. Lecture Notes in Computer Science 686, Springer-Verlag, 1993, pp. 55–62.
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© 1995 Springer-Verlag Berlin Heidelberg
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Joya, G., Sandoval, F. (1995). A neural paradigm for controlling autonomous systems with reflex behaviour and learning capability. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_187
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DOI: https://doi.org/10.1007/3-540-59497-3_187
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Online ISBN: 978-3-540-49288-7
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