  In this paper we examine learning methods combining the Random Neural
Network, a biologically inspired neural network and the Extreme Learning
Machine that achieve state of the art classification performance while
requiring much shorter training time. The Random Neural Network is a integrate
and fire computational model of a neural network whose mathematical structure
permits the efficient analysis of large ensembles of neurons. An activation
function is derived from the RNN and used in an Extreme Learning Machine. We
compare the performance of this combination against the ELM with various
activation functions, we reduce the input dimensionality via PCA and compare
its performance vs. autoencoder based versions of the RNN-ELM.
