Nonlinear factorization in sparsely encoded Hopfield-like neural networks

The problem of binary factorization of complex patterns in recurrent Hopfieldlike neural network was studied both theoretically and by means of computer simulation. The number and sparseness of factors mixed in patterns crucially determines the ability of an autoassociator to perform a factorization. Basing on experimental data on memory and learning one may suggest, that there exists a neural system of intermediate storage of information, which fulfills the function of binary factorization of the incoming polysensory information for its further effective storage in the form of elementary associatively bound factors. We suppose that field CA3 of the hippocampus possessing all properties of the autoassociative memory performs such function. This functional idea could be fruitfully applied to various memory related tasks (e.g. spatial navigation) and lead to some critical experiments.