Abstract
This work introduces complex processing neural network topologies, based on the concept of modulating neuron, which induce higher order terms by means of the modulation of the synaptic weights. These structures present the advantages of being very easy to train, adapting easily to changing contexts and offer very good generalization capabilities along all the dimensions of the problems they are trained to solve. Finally, the function each modulation level or each module performs is very clear, making it simple to extend the model to multilevel hierarchies.
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References
Giles, C. L., and Maxwell, T., Learning, Invariance, and Generalization in High-Order Neural Networks, Applied Optics, Vol. 26, No. 23, December 1987.
McCulloch, W. S., and Pitts, W., A Logical Calculus of the Ideas Immanent in Neural Activity, Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, 1943.
Hawkins, R. D., Abrams, W.T., Carew, T.J., and Kandel, E.R., A Cellular Mechanism of Classical Conditioning in Aplysia: Activity Depedent Amplification of Presynaptic Facilitation, Science, Vol. 219, pp. 400–405, January 1983.
Silva, A. J., Stevents, C.F., Tonehawa, S., and Wang, Y. Y., Defficient Hippocampal Long Term Potentiation in α-Calcium-Calmodulin Kinase-II Mutant Mice. Science 257, No. 5067, pp 201–206, 1992.
Dehaene, S., Changeux, J., and Nadal, J., Neural Networks that Learn Temporal Sequences by Selection, Procc. Natl. Acad. Sci. USA, Vol. 84, pp. 2727–2731, May 1987.
Dehaene, S., and Changeux, J., Neuronal Models of Cognitive Functions, Cognition 33, pp, 63–109, 1989.
Peretto, P., and Niez, J.J., Long Term Memory Storage Capacity of Multiconnected Neural Networks, Biol. Cybern. 54, pp. 53–63, 1986.
Duro, R. J., Santos, J., and, Sarmiento, A., GENIAL, an Evolutionary Recurrent Neural Network Designer and Trainer. In Proceedings of CAST'94 (Fourth International Workshop on Computer Aided Systems Technology), Ottawa, Canada, May 1994.
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© 1995 Springer-Verlag Berlin Heidelberg
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Duro, R.J., Santos, J., Gómez, A. (1995). Synaptic modulation based artificial neural networks. 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_153
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DOI: https://doi.org/10.1007/3-540-59497-3_153
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