  In spiking neural networks (SNN), at each node, an incoming sequence of
weighted Dirac pulses is converted into an output sequence of weighted Dirac
pulses by a leaky-integrate-and-fire (LIF) neuron model based on spike
aggregation and thresholding. We show that this mapping can be understood as a
quantization operator and state a corresponding formula for the quantization
error by means of the Alexiewicz norm. This analysis has implications for
rethinking re-initialization in the LIF model, leading to the proposal of
'reset-to-mod' as a modulo-based reset variant.
