Fly olfactory circuit wiring offers no clear retrieval edge over random connections
A Connectome Test of the Fly Hashing Algorithm
Neural and Evolutionary Computing
Summary
The authors checked if the specific wiring of a fruit fly’s smell-processing brain area helps computers quickly find similar smells, using detailed brain maps. They found that a simple random wiring, which keeps how busy each part is but changes exact connections, works just as well or better. This suggests that the special wiring of fly brains might not be needed to build these fast searching tools. The work also shows that the fly’s method is more efficient when measured by active parts rather than computational steps.
What this means in practice
- •For hardware designers: Design hardware accelerators that minimize active units for efficient locality-sensitive hashing inspired by fly olfactory circuits.
- •For machine learning engineers: Implement fast similarity search methods using sparse random projections without needing detailed biological wiring data.
Authors
Sebastian Senge
Abstract
Dasgupta, Stevens and Navlakha (2017) showed that the Drosophila olfactory circuit, modelled as a random sparse projection followed by winner-take-all, is a locality-sensitive hash that beats classical LSH. The projection was random because the wiring was unknown. We test it against four electron-microscopy connectomes (MaleCNS, hemibrain, FlyWire, BANC; four animals, seven hemispheres). First, the 2017 pattern holds in a reimplementation of its protocol on SIFT, MNIST and odour mixtures (GloVe is near chance at short codes for every method): the fly hash beats k Gaussian projections at short hash lengths (3.1x in AP@200 on MNIST at k = 4). Second, against the tested real-valued Gaussian baseline that advantage is per active cell, not per operation: Gaussian projections given the same projection arithmetic retrieve better on every dataset and input dimension tested. Third, across four connectomes the measured pairing of glomeruli gives no consistent retrieval advantage over degree-preserving rewiring: retrieval is slightly lower (median -1.6%), and the small odour deficits depend on how missing odour responses are treated. Separately, equalising glomerular fan-out at fixed connection count improves retrieval in the model in every hemisphere, while equalising inputs per cell lowers it on average. Yet the fan-out profile is similar across the four sampled animals (median between-animal Spearman rho = 0.86), structural synapse counts do not offset it, and its relation to odour tuning is weak. In the model that uneven allocation costs retrieval. For practice: measured wiring gives no consistent retrieval advantage over degree-preserving random wiring, so a fly hash needs no connectome data, and its advantage is per active unit, which may suit hardware where active units rather than arithmetic are the binding cost, a hypothesis we do not test.