Why Do SNNs Convert but Not Train?

Why does a spiking neural network (SNN) converted from a trained Transformer nearly retain its accuracy, but one trained from scratch fall behind a Transformer of the same size? If spikes set the limit, conversion and its long integration windows are the better route; if training sets it, the limit has to be located first. We study this question through the release identity of the spiking neuron. At the non-leaky endpoint of conversion a neuron integrates linearly, whereas in the leaky regime of training its release depends on when past inputs arrived, a dependence to be learned. By the chain rule, a past token reaches the prediction, and its gradient the parameters, only through four links, in which it is written into a membrane, released, read out and credited. For each link we derive why gradient training leaves it closed and which temporal prior opens it, and we build these priors into a spiking language model, NeuronSpark. Trained from scratch at four scales, NeuronSpark reaches the level of mainstream architectures of the same size in training loss and BabyLM evaluations. Register-transfer-level estimates give it the lowest energy per token, with an advantage that grows with scale.