The trained networks this entry uses are produced once in the shared training hub, exp022 (Training), and reused here rather than retrained.
The Δt audit asks whether the exp025 headline E rate is a physical (Hz) property of the trained network or an artefact of the integration timestep. The rate stays within a 9–14 Hz band across a 20× Δt sweep, accuracy holds at 90.4–91.4%, and the gamma cycle period in physical ms is invariant. The rate is not fully Δt-independent, though: it rises monotonically with the timestep, from ≈ 9.2 Hz at Δt = 0.05 ms to ≈ 13.4 Hz at Δt = 1 ms, so a coarser step inflates the rate while leaving accuracy and cycle period intact.
| Parameter | Value |
| Integration timestep | 0.05–1.0 ms (swept) |
| Trial duration | 200 ms |
| MNIST samples (80/20 stratified split of 7000) | 5600 train / 1400 test (≈ 10% of the 70k-sample MNIST corpus) |
| Epochs | 50 |
The exp022 hub trains one PING per ms × seed = 15 cells; this entry loads them and evaluates. Total physical time ms is held constant (step count varies 4000 → 200). Batch size 64 throughout, smaller than exp025′s 256 but matched across the sweep so per-step compute and memory stay comparable, and the Δt = 0.05 cells (4000 timesteps × × ) fit in a single A100. All other PING recipe parameters held to exp025.
Run inference on the test set; report mean E rate (Hz), accuracy, and a single-trial raster from seed 42 per Δt for visual cycle-period inspection.