In this paper the standard Echo State approach is combined with a topography, i.e. it is assigned with a position which implies certain constraints of the mutual connectivity between these neurons. The overall design of the network allows certain neurons to process new information earlier than others. As a consequence the connectivity of the trained output layer can be analyzed; conclusions can be drawn regarding which reservoir depth is sufficient to process the given task. In particular we look at connection strengths of different locations of the reservoir as a function of the test error which can be influenced by using ridge regression. © 2010 Springer-Verlag.
CITATION STYLE
Mayer, N. M., Browne, M., & Wu, H. J. (2010). ESNs with one dimensional topography. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6444 LNCS, pp. 209–216). https://doi.org/10.1007/978-3-642-17534-3_26
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