Aniviza Research
Structure, dynamics, capability and energy across a 10× ladder
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.
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.
| Quantity | Value |
|---|---|
| Neurons (FlyWire FAFB v783, 1×) | 139,255 |
| Thresholded connections (1×) | 2,700,513 |
| Synapses (1×) | 34,153,566 |
| Connection scaling exponent | N^1.0175 (R²=0.9995) |
| Neurons at 10× | 1,392,550 |
| Connections at 10× | 27.8M |
| Best connectivity predictor | 32-D spectral embedding (AUC 0.952) |
| Hardware energy across ladder (GPU J) | 438 / 500 / 946 / 2022 |
| Cost per spike across ladder | 8.9 → 4.2 μJ |
| Compiler defects surfaced by the Vyb path | 12 |
C(N) growing faster than P(N)) — not supported