Application of Hopfield-like Neural Networks to Nonlinear Factorization
Dušan Húsek, Alexander Frolov, Hana Řezanková, Václav Snåšel · COMPSTAT · 2002
The problem of binary factorization of complex patterns in recurrent Hopfield-like neural network was studied by means of computer simulation. The network ability to perform a factorization was analyzed depending on the number and sparseness of factors mixed in presented patterns. Binary factorization in sparsely encoded Hopfield-like neural network is treated as efficient statistical method and as a functional model of hippocampal CA3 field