Selective association is enabled by changing the size of the window in each neuron.
It means that our model requires neither complicated learning nor advance knowledge on the correlational structure of the memory patterns. We have established a quantitative theory of neuro-window networks based on the statistical methods. The theory gives the theoretical values of memory capacity and basin of attraction which fits the results of the numerical simulations. We have developed a fast algorithm which enables parallel and digital computers to find quasi-optimal solutions in the combinatorial optimization problems, such as partition problems and traveling salesman problems. The proposed algorithm removes the minimum eigenvalue component of the weight matrix.