  Grand efforts in neuroscience are working toward mapping the connectomes of
many new species, including the near completion of the Drosophila melanogaster.
It is important to ask whether these models could benefit artificial
intelligence. In this work we ask two fundamental questions: (1) where and when
biological connectomes can provide use in machine learning, (2) which design
principles are necessary for extracting a good representation of the
connectome. Toward this end, we translate the motor circuit of the C. Elegans
nematode into artificial neural networks at varying levels of biophysical
realism and evaluate the outcome of training these networks on motor and
non-motor behavioral tasks. We demonstrate that biophysical realism need not be
upheld to attain the advantages of using biological circuits. We also establish
that, even if the exact wiring diagram is not retained, the architectural
statistics provide a valuable prior. Finally, we show that while the C. Elegans
locomotion circuit provides a powerful inductive bias on locomotion problems,
its structure may hinder performance on tasks unrelated to locomotion such as
visual classification problems.
