  This article provides a background and descriptive analysis of insect memory
and the coding of olfactory sensation in Drosophila, presenting graphs and
summary statistics from a large dataset of neurons and synapses that was
recently made publicly available and also discussing findings from the existing
empirical literature. Some general principles from Drosophila olfaction are
discussed as they apply to the design of analogous systems in artificial neural
networks: (1) the networks used for coding are shallow; (2) the level of
connectedness varies widely across neurons in the same layer; (3) much
communication is between neurons in the same layer; (4) in most olfactory
learning, the manner in which sensory inputs are represented in stored memory
is largely fixed, and the learning process involves developing positive or
negative associations with existing categories of inputs.
