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This study compares the modularity performance of two artificial neuralnetwork architectures: a Long Short-Term Memory (LSTM) recurrent network, andMorphognosis, a neural network based on a hierarchy of spatial and temporalcontexts. Mazes are used to measure performance, defined as the ability toutilize independently learned mazes to solve mazes composed of them. A maze isa sequence of rooms connected by doors. The modular task is implemented asfollows: at the beginning of the maze, an initial door choice forms a contextthat must be retained until the end of an intervening maze, where the same doormust be chosen again to reach the goal. For testing, the door-association mazesand separately trained intervening mazes are presented together for the firsttime. While both neural networks perform well during training, the testingperformance of Morphognosis is significantly better than LSTM on this modulartask.

A modularity comparison of Long Short-Term Memory and Morphognosis neural networks