Which model to use for the Liquid State Machine?

Abstract

The properties of separation ability and computational efficiency of liquid state machines depend on the neural model employed and on the connection density in the liquid column. A simple model of part of mammalians visual system consisting of one hypercolumn was examined. Such a system was stimulated by two different input patterns, and the Euclidean distance, as well as the partial and global entropy of the liquid column responses were calculated. Interesting insights could be drawn regarding the properties of different neural models used in the liquid hypercolumn, and on the effect of connection density on the information representation capability of the system.