A morphological auto-associative memory based on dendritic computing

This paper presents a model of an artificial neural network whose neurons are endowed with dendritic structures and have a computational framework based on lattice algebra. Such neurons bear closer resemblance to their biological counterpart than other current artificial models. Employing a two-layer dendritic network model, we construct an auto-associative memory which is robust in the presence of random noise. Furthermore, unlike the kernel method, this memory does not require that the original patterns satisfy any conditions of morphological independence.