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.

翻译：在脉冲神经网络（SNN）中，每个节点处，基于脉冲聚合与阈值触发的漏积分-点火（LIF）神经元模型，将输入的加权狄拉克脉冲序列转换为输出的加权狄拉克脉冲序列。研究表明，该映射可被理解为一种量化算子，并通过Alexiewicz范数给出了相应的量化误差公式。这一分析为重新思考LIF模型中的重置机制提供了启示，进而提出基于模运算的"重置至模"重置变体方法。