Learning Local Mixtures of Temporal Codes for Spiking Neural Networks

Temporal codes determine how a static image drives a spiking neural network (SNN) over time. A pointwise encoder applies the same coding rule across an image, although equal normalized intensities can occur in different local structures. This raises an allocation question: how should a bank of temporal codes be combined across spatial locations? We introduce FAPE, a framework that learns this allocation from local image context. FAPE constructs binary Burst, time-to-first-spike (TTFS), and Rate candidates in parallel and uses multiscale features to predict normalized mixture weights at each location and simulation step. The resulting continuous currents drive an unchanged SNN backbone, with optional temporal conditioning parameterized separately. On validation-selected CIFAR-100 models, spatial-only FAPE achieves 69.21 ± 0.19% official-test top-1 accuracy across three training seeds, exceeding parameter-count-matched input-global and fixed-input gates by 1.90 and 3.18 percentage points. Both paired gains are positive for every seed. On the 1.28-million-image ImageNet32 benchmark, spatial-only FAPE exceeds the globally learned mixture by 2.86 points under a shared 90-epoch protocol. Direct min–max current reaches 74.24 ± 0.41% on CIFAR-100, identifying a stronger alternative input representation. Follow-up retraining with time-averaged, repeated inputs improves selected validation accuracy in all nine matched method–seed comparisons, while reducing the mean allocation contrasts. Together, these results connect local-allocation gains to the candidate representation and its presentation to the backbone.