Aniviza Research

FlyScale: geometric scaling of the adult Drosophila connectome

Structure, dynamics, capability and energy across a 10× ladder

Rick Goldberg · September 2026 · 25 pages

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Abstract

Can a biological connectome be enlarged? We take the complete adult Drosophila connectome (FlyWire FAFB v783; 139,255 neurons, 2,700,513 thresholded connections, 34,153,566 synapses) and ask whether a synthetic graph can be generated that preserves its structural statistics, its propagation dynamics, its behavioral capabilities and its energy budget as it grows.

The method is a geometric one. We first fit latent coordinates to the connectome and ask which space actually predicts connectivity: a 32-dimensional spectral embedding predicts held-out connections best (AUC 0.952), a 2-D hyperbolic embedding beats the anatomical 3-D soma coordinates (0.865 vs 0.851), and no embedding beats a degree-only baseline by a margin larger than its own run-to-run spread. Scaling then proceeds by node subdivision in that geometry: each neuron is replaced by c children placed near it, each inheriting the parent's partner set and synapse weights, with the concrete target chosen by the fitted connection law P(connect | distance) = 1/(1+e^((d-R)/T)).

The result is a ladder of graphs at 2×, 5× and 10× (up to 1,392,550 neurons and 27.8M connections) whose connection count scales as N^1.0175 (R^2=0.9995) — sparsity is preserved, mean degree and mean connection strength stay bounded — and which renormalizes back to the biological graph almost exactly. Capability grows on some axes (stimulus-discrimination information ∝ N^1.014, memory capacity ≥ 96 associations versus 8 at 1×) and is flat on others. Energy is measured on two tracks that are never summed: hardware (438/500/946/2022 GPU joules across the ladder, with cost per spike falling from 8.9 to 4.2 μJ) and biological-equivalent.

The work is deliberately dual-implementation: a Python reference oracle and a Vyb-native production path — loader, discrete-event runtime, GPU kernels — and the effort to build the second one surfaced twelve compiler defects, of which the blocking one, a kernel-mode store that wrote eight bytes into a four-byte slot, is documented, fixed upstream and verified here. We report what failed as carefully as what worked.

Discrete event simulation at the core

The paper's production path runs the connectome as a discrete event simulation: the network is advanced event by event on a Vyb-native runtime, rather than in fixed time steps. Each scale of the ladder is simulated this way, and cross-scale comparisons are made valid by a protocol fingerprint (39f6c75cde6548e8) that is verified to hold at all four scales — so a difference between scales cannot be a protocol difference. Spike events, propagation depth and sequence depth are all measured from these runs.

Key results

QuantityValue
Neurons (FlyWire FAFB v783, 1×)139,255
Thresholded connections (1×)2,700,513
Synapses (1×)34,153,566
Connection scaling exponentN^1.0175 (R²=0.9995)
Neurons at 10×1,392,550
Connections at 10×27.8M
Best connectivity predictor32-D spectral embedding (AUC 0.952)
Hardware energy across ladder (GPU J)438 / 500 / 946 / 2022
Cost per spike across ladder8.9 → 4.2 μJ
Compiler defects surfaced by the Vyb path12

Verdict against the project brief