Dendritic lattice associative memories for pattern classification

We present a two layer dendritic hetero-associative memory that gives high percentages of correct classification for typical pattern recognition problems. The memory is a feedforward dendritic network based on lattice algebra operations and can be used with multivalued real inputs. A major consequence of this approach shows the inherent capability of prototype-class pattern associations to realize classification tasks in a direct and fast way without any convergence problems.