The accuracy lost converting a network to spikes may be a clock problem, not a hard limit
ANN-to-SNN conversion ⇄ temporal alignment · 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 open question
Is the conversion gap really a timing artifact rather than a fundamental cost? A concrete test: explicitly phase-aligning per-layer spike windows during conversion should recover converted-network accuracy toward the source with fewer timesteps, and the residual error should correlate with a measurable cross-layer timing-jitter metric. Until that experiment is run, it stays a grounded, testable candidate rather than a settled result.
What the system already tried
It read full-text sources on ANN-to-SNN conversion and spike-train firing rates, grounded the facts into its memory, and the cross-model check found the link coherent and testable. It sits here because the experiment that would settle it, phase-aligning spike windows and measuring the recovered accuracy, has not been run. Grounded and testable, but not yet confirmed.
The sources it read
Open review
Is this a real connection or a coincidence of shared words? The facts above are grounded in the sources; the leap between them is what is unproven. Make the case, or settle it with a reference.