Nonlinear factorization in the hippocampal neural structure

An intrinsic factor analysis (factorization) framework for information redundancy elimination by means of Hebbian learning in sparsely encoded Hopfield-like neural networks is presented. Computer simulations revealed that the information redundancy, which can be eliminated by factorization, is sparseness dependent. Due to strong similarity of Hopfield-like neural networks to that of CA3 field of the hippocampus and following Marr's ideas we propose a physiological mechanism for redundancy elimination (fractorization) in CA3 and further replay to neocortex in the form of "classificatory units".