Self-Organizing Maps and Scale-Invariant Maps in Echo State Networks

Abstract

In the last years a new approach for designing and training artificial Recurrent Neural Network (RNN) have been investigated under the name of Reservoir Computing (RC). One important model in the field of RC has been developed under the name of Echo State Networks (ESNs). Traditionally, an ESN uses a RNN with random untrained parameters called the reservoir. The Self-Organizing Map (SOM) and the Scale Invariant Map (SIM) are two methods of topographic maps which have been used in different tasks of unsupervised learning. Recently, new works show that is effective using the SOM to set values of the reservoir parameters. The primary goal of this work is to improve the performance of ESN using the another method SIM. Here, we present the description of these two topographic map methods and the way to apply its on the ESN initialization. We specify an original algorithm to set the reservoir weights using the SOM and SIM. Furthermore, we use artificial data set to compare the use of topographic maps to initialize the ESN with random initialization. Overall, our results show the aptitude of SIM and SOM to set the reservoir parameters.