Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks
LT-Gate is a neuron model for spiking neural networks that learns continuously across changing environments by combining dual time-constant dynamics with an adaptive gating mechanism. This addresses the stability–plasticity dilemma by allowing neurons to track both fast and slow signals simultaneously. The model achieves superior performance in sequential learning tasks while maintaining hardware compatibility.