  Machine learning (ML) classifiers always benefit from more informative input
features. We seek to auto-generate stronger feature sets in order to address
the difficulty that ML methods often experience given limited training data. A
wide range of biological neural nets (BNNs) excel at fast learning, implying
that they are adept at extracting informative features. We can thus look to
BNNs for tools to improve ML performance in this low-data regime. The insect
olfactory network learns new odors very rapidly, by means of three key
elements: A competitive inhibition layer; a high-dimensional sparse plastic
layer; and Hebbian updates of synaptic weights.
  In this work, we deployed MothNet, a computational model of the insect
olfactory network, as an automatic feature generator: Attached as a front-end
pre-processor, its Readout Neurons provided new features, derived from the
original features, for use by standard ML classifiers. We found that these
"insect cyborgs", i.e. classifiers that are part-insect model and part-ML
method, had significantly better performance than baseline ML methods alone on
a vectorized MNIST dataset. The MothNet feature generator also substantially
out-performed other feature generating methods such as PCA, PLS, and NNs, as
well as pre-training to initialize NN weights. Cyborgs improved relative test
set accuracy by an average of 6% to 33% depending on baseline ML accuracy,
while relative reduction in test set error exceeded 50% for higher baseline
accuracy ML models. These results indicate the potential value of BNN-inspired
feature generators in the ML context.
