Dynamical Learning Process for Recognition of Correlated Patterns in Symmetric Spin Glass Models

In the framework of spin-glass models with symmetric ( multi-spin ) interactions of even order a local dynamical learning process is studied, by which the energy landscape is modified systematically in such a way that even strongly correlated noisy patterns can be recognized. Additionally the basins of attraction of the patterns can be systematically enlarged by performing the learning process with noisy patterns. After completion of the learning process the system typically recognizes for two-spin interactions as many patterns as there are neurons ( p ≃ Nm−1 for m-spin interactions ), and for small systems even more ( p > N for m = 2 ).