Abstract
An infinite range neural network works as an associative memory device if both the learning storage and attractor abilities are large enough. This work deals with the search of an optimal topology, varying the (small-world) parameters: the average connectivity γ ranges from the fully linked to a extremely diluted network; the randomness ω ranges from purely neighbor links to a completely random network. The network capacity is measured by the mutual information, MI, between patterns and retrieval states. It is found that MI is optimized at a certain value γ o for a given 0 < ω< 1 if the network is asymmetric.
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Dominguez, D., Koroutchev, K., Serrano, E., Rodríguez, F.B. (2004). Mutual Information and Topology 1: Asymmetric Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_3
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DOI: https://doi.org/10.1007/978-3-540-28647-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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