Spiking Neural Networks (SNNs) are a promising framework for event-driven temporal processing. Prior work has improved temporal modeling through richer neuron dynamics and network-level mechanisms such as recurrence and delays, but it remains unclear how individual spiking neurons should specialize within a network. In this work, we introduce FiTS, a spiking neuron that factorizes temporal computation within each neuron into Frequency Selectivity (FS) and Temporal Shaping (TS). The FS module parameterizes each neuron's target frequency as the maximizer of its subthreshold magnitude response, while the TS module reshapes when frequency components contribute to membrane voltage accumulation through group-delay modulation. On auditory benchmarks where frequency selectivity and timing are central to the input structure, FiTS consistently improves over a plain Leaky Integrate-and-Fire (LIF) baseline in simple feedforward SNNs without recurrence or network-level delays, while remaining competitive with strong temporal SNN baselines. Beyond accuracy, the learned target frequencies and group-delay shifts provide interpretable neuron-level summaries of the frequency and timing organization learned within the network.

翻译：脉冲神经网络（SNN）是事件驱动时序处理的一种有前景框架。先前工作通过更丰富的神经元动力学以及循环连接和延迟等网络级机制提升了时序建模能力，但单个脉冲神经元在神经网络中应如何实现功能特化仍不明确。本文提出FiTS——一种将每个神经元内部时序计算分解为频率选择（FS）与时间塑形（TS）的脉冲神经元。FS模块将每个神经元的目标频率参数化为其亚阈值幅值响应的最大化参数，TS模块则通过群延迟调制重塑频率分量对膜电位积累的贡献时序。在频率选择性和时序特性构成输入结构核心的听觉基准任务中，FiTS在不依赖循环连接或网络级延迟的简单前馈SNN中持续优于普通泄漏积分点火（LIF）基线，同时与强时序SNN基线保持竞争力。除精度的提升外，学习得到的目标频率和群延迟偏移为网络内部习得的频率与时间组织提供了可解释的神经元级摘要。