Data-based analysis of Laplacian Eigenmaps for manifold reduction in supervised Liquid State classifiers

The manuscript introduces a data-driven technique founded on Laplacian Eigenmaps for manifold reduction in bio-inspired Liquid State classifiers. Starting from a preliminary in- troduction about the algorithm and the need of using manifold reduction methods for data representation, a statistical analysis of hyperparameters involved in the Laplacian Eigen- maps technique is presented and the effects of quantisation on trained weights is dis- cussed with a view to efficiently implement multiple parallel mappings in the digital do- main.