Non-Linear Statistical Analysis and Self-Organizing Hebbian Networks

Neurons learning under an unsupervised Hebbian learning rule can perform a nonlinear generalization of principal component analysis. This relationship between nonlinear PCA and nonlinear neurons is reviewed. The stable fixed points of the neuron learning dynamics correspond to the maxima of the statist,ic optimized under non(cid:173) linear PCA. However, in order to predict. This is shown for a simple model. Methods of statistical mechanics can be used to find the optima of the objective function of non-linear PCA.