  Can connectome-derived constraints inform computation? In this paper we
investigate the contribution of a fruit fly connectome's topology on the
performance of an Echo State Network (ESN) -- a subset of Reservoir Computing
which is state of the art in chaotic time series prediction. Specifically, we
replace the reservoir layer of a classical ESN -- normally a fixed, random
graph represented as a 2-d matrix -- with a particular (female) fruit fly
connectome-derived connectivity matrix. We refer to this experimental class of
models (with connectome-derived reservoirs) as "Fruit Fly ESNs" (FFESNs). We
train and validate the FFESN on a chaotic time series prediction task; here we
consider four sets of trials with different training input sizes (small, large)
and train-validate splits (two variants). We compare the validation performance
(Mean-Squared Error) of all of the best FFESN models to a class of control
model ESNs (simply referred to as "ESNs"). Overall, for all four sets of trials
we find that the FFESN either significantly outperforms (and has lower variance
than) the ESN; or simply has lower variance than the ESN.
