An improved learning algorithm for laterally interconnected synergetically self-organizing map

Abstract

LISSOM (Laterally Interconnected Synergetically Self-Organizing Map) is a biologically motivated self-organizing neural network for the simultaneous development of topographic maps and lateral interactions in the visual cortex. However, the simple Hebbian mechanism for afferent connections requires a redundant dimension to be added to the input, and normalization is necessary. Another shortcoming of LISSOM is that several parameters must be chosen before it can be used as a model of topographic map formation. To solve these problems, we propose to apply the least mean-square error reconstruction (LMSER) learning rule as an alternative to the simple Hebbian rule for the afferent connections. Experiments demonstrate the essential topographic map properties from the improved LISSOM model.