Scheme of Spi-Fly vs real fly olfactory system (IMAGE)
Caption
On the left, the brain circuitry for olfaction (classifying smells) in fruit flies is represented as circles (different types of neurons) and arrows (active pathway connections). On the right, the fruit fly-inspired Spi-Fly architecture.
In fruit flies, odor receptor neurons (ORNs) sense different scents in the environment, activating projection neurons (PNs). Signals are normalized — adjusted to be within a particular range — by local neurons (LNs). PNs and Kenyon cells (KCs), of which there are around 2000, make lots of random connections, to expand each signal. Then, to reduce overlap between different signals, 95% of KCs are inhibited by another neuron (the APL), so just a few remain active for each different scent. These active KCs speak to output neurons (labelled here as MBONs). Through this dimension expanding and shrinkage, the unique pathways enable identification of each smell. If new smells are encountered, a learning algorithm at the connection between KCs and MBONs enables fast, continuous learning.
Spi-Fly is essentially a smaller version of this scheme, exhibiting the same dimension expansion and contraction, but using less layers. The active neurons in the hidden middle layer act as a unique bar code, which can be recognized by output neurons with the help of an associative learning rule.
Credit
Max et al., Neuromorphic Computing and Engineering, 2026, DOI: 10.1088/2634-4386/ae9177
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License
CC BY-NC