ESNs with One Dimensional Topography

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

ESNs with one dimensional topography

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