A Time-Variant Spiking Neuron from a Unified State-Dynamics Perspective

Spiking neural networks (SNNs) have increasingly incorporated rich neuronal dynamics to enhance temporal processing. However, richer dynamics do not necessarily enable neurons to adapt how information is processed over time. To examine this issue systematically, we introduce a unified state-dynamics framework that characterizes diverse spiking neurons through a common formulation and enables direct comparison of their dynamical designs. The framework reveals a common limitation of existing neurons: although they enhance temporal processing through richer internal states and state evolution, the dynamical rules determining how inputs are integrated, how states evolve, and how they are read out generally remain unchanged throughout temporal processing. Such time-invariant rules limit the ability of neurons to adjust information processing as information relevance changes over time. Inspired by time-varying regulation in biological neurons, we propose the Time-Variant Multi-Compartment Neuron (TV-MCN). TV-MCN builds on multiscale compartment dynamics and makes the rules for input integration, state evolution, and state readout time-varying according to the current input. Specifically, an input-dependent internal time regulates the rate of state evolution, while the current input dynamically modulates input integration and state readout. Extensive experiments show that TV-MCN achieves strong overall performance, with particularly large gains on tasks requiring selective retention and retrieval of information, while ablation studies further support the effectiveness of its time-varying mechanisms.