  The insect olfactory system, which includes the antennal lobe (AL), mushroom
body (MB), and ancillary structures, is a relatively simple neural system
capable of learning. Its structural features, which are widespread in
biological neural systems, process olfactory stimuli through a cascade of
networks where large dimension shifts occur from stage to stage and where
sparsity and randomness play a critical role in coding. Learning is partly
enabled by a neuromodulatory reward mechanism of octopamine stimulation of the
AL, whose increased activity induces rewiring of the MB through Hebbian
plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that
are the most important for the representation of the learned odor. Based upon
current biophysical knowledge, we have constructed an end-to-end computational
model of the Manduca sexta moth olfactory system which includes the interaction
of the AL and MB under octopamine stimulation. Our model is able to robustly
learn new odors, and our simulations of integrate-and-fire neurons match the
statistical features of in-vivo firing rate data. From a biological
perspective, the model provides a valuable tool for examining the role of
neuromodulators, like octopamine, in learning, and gives insight into critical
interactions between sparsity, Hebbian growth, and stimulation during learning.
Our simulations also inform predictions about structural details of the
olfactory system that are not currently well-characterized. From a machine
learning perspective, the model yields bio-inspired mechanisms that are
potentially useful in constructing neural nets for rapid learning from very few
samples. These mechanisms include high-noise layers, sparse layers as noise
filters, and a biologically-plausible optimization method to train the network
based on octopamine stimulation, sparse layers, and Hebbian growth.
