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Abstract: In the present paper a detailed taxonomy of neural network models with various restrictions is presented with respect to their computational properties. The criteria of classification include e.g. feedforward and recurrent architectures, discrete and continuous time, binary and analog states, symmetric and asymmetric weights, finite size and infinite families of networks, deterministic and probabilistic models, etc. The underlying results concerning the computational power of perceptron, RBF,... (Update)
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BibTeX entry: (Update)
@article{ sima00computational,
author = "Sima and Orponen and Antti-Poika",
title = "On the Computational Complexity of Binary and Analog Symmetric Hopfield Nets",
journal = "NEURCOMP: Neural Computation",
volume = "12",
year = "2000",
url = "citeseer.ist.psu.edu/sima00computational.html" }
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