Grayscale image recall from imperfect inputs with a two layer dendritic lattice associative memory

We present a two layer dendritic auto-associative memory with high rates of perfect recall of exemplar grayscale images distorted by different transformations or corrupted by random noise. The memory is a feedforward network based on dendritic computing employing lattice algebraic operations and is capable of dealing with real valued inputs. A major consequence of this approach is the direct and fast association of perfect or imperfect input patterns with stored associated patterns without any convergence problems.