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