The accuracy lost converting a network to spikes may be a clock problem, not a hard limit
ANN-to-SNN conversion ⇄ temporal alignment · bridged via firing rate
The latency and accuracy gap introduced when an artificial neural network is converted to a spiking neural network may track a single quantity, how well spike-train firing rates are temporally aligned across layers, rather than being an irreducible cost of spiking, suggesting the gap is a synchronization artifact, not a fundamental limit.
The testable prediction
If the gap is alignment-driven, then explicitly phase-aligning per-layer spike windows during conversion should recover converted-SNN accuracy toward the source ANN with fewer timesteps, and the residual error should correlate with a measurable cross-layer timing-jitter metric.
The jury
Model A: KEEP ✓Model B: KEEP ✓ · testable gate: on
Two models from different families had to both keep it, and neither judged its own phrasing. That cross-model agreement is the gate; it is why a fabricated or trivial bridge does not reach this page.
The receipts: the sources
The bridged facts were extracted from these public sources and verified against them. The connection is proposed by AI; the underlying facts are grounded, not invented. Someone may have reached this idea before, and we make no claim to being first. What we can show is that it came from the facts, not from copying anyone.
What the jury threw out
From the same run: 10 connections proposed, 2 survived. Here are candidates the jury killed, and why: the part most AI tools hide.