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. Show more