  We propose an architecture and process for using the Iterated Learning Model
("ILM") for artificial neural networks. We show that ILM does not lead to the
same clear compositionality as observed using DCGs, but does lead to a modest
improvement in compositionality, as measured by holdout accuracy and topologic
similarity. We show that ILM can lead to an anti-correlation between holdout
accuracy and topologic rho. We demonstrate that ILM can increase
compositionality when using non-symbolic high-dimensional images as input.
