Perfect recall from noisy input patterns with a dendritic lattice associative memory

We introduce a methodology for constructing an associative memory that is highly robust in the presence of noisy inputs. The memory is based on dendritic computing employing lattice algebraic operations. A major consequence of this approach is the avoidance of convergence problems during the training phase and rapid association of perfect and nonperfect input patterns with stored associated patterns.