A multi-winners self-organizing neural network

We propose a 2-layer self-organizing neural network which can store information distributedly. The proposed network has two phases for the information processing: the storage phase and the recall phase. In the storage phase, the proposed network represents information distributedly by plural excited neurons using the proposed multi-winners competitive dynamics. Then, the weights between two layers are learned using error correction learning. In the recall phase, the trained proposed network can recall the stored pattern which is the closest to a presented pattern. We carried out computer simulations to confirm the validity of the proposed network