Our work in this area includes extending the Self-Organizing Map architecture (SOM; Kohonen, 1982; 1997; von der Malsburg, 1975) with lateral connections, hierarchies, sequential inputs, and growing network structures. This work has been done mainly with cognitive science and computational neuroscience applications in mind, as described in the visual cortex, concept and schema learning, episodic memory, and natural language processing pages. We have also applied such maps to character recognition, visualizing high-dimensional data, modeling multi-sensory integration, and speech recognition, as described below.