  Fruit flies are established model systems for studying olfactory learning as
they will readily learn to associate odors with both electric shock or sugar
rewards. The mechanisms of the insect brain apparently responsible for odor
learning form a relatively shallow neuronal architecture. Olfactory inputs are
received by the antennal lobe (AL) of the brain, which produces an encoding of
each odor mixture across ~50 sub-units known as glomeruli. Each of these
glomeruli then project its component of this feature vector to several of ~2000
so-called Kenyon Cells (KCs) in a region of the brain known as the mushroom
body (MB). Fly responses to odors are generated by small downstream neuropils
that decode the higher-order representation from the MB. Research has shown
that there is no recognizable pattern in the glomeruli--KC connections (and
thus the particular higher-order representations); they are akin to
fingerprints~-- even isogenic flies have different projections. Leveraging
insights from this architecture, we propose KCNet, a single-hidden-layer neural
network that contains sparse, randomized, binary weights between the input
layer and the hidden layer and analytically learned weights between the hidden
layer and the output layer. Furthermore, we also propose a dynamic optimization
algorithm that enables the KCNet to increase performance beyond its structural
limits by searching a more efficient set of inputs. For odorant-perception
tasks that predict perceptual properties of an odorant, we show that KCNet
outperforms existing data-driven approaches, such as XGBoost. For
image-classification tasks, KCNet achieves reasonable performance on benchmark
datasets (MNIST, Fashion-MNIST, and EMNIST) without any data-augmentation
methods or convolutional layers and shows particularly fast running time. Thus,
neural networks inspired by the insect brain can be both economical and perform
well.
