Event-Driven Parameter Dynamics for Spatiotemporal Parameter-Update Application in Deep Learning

Standard neural network training typically applies optimizer-generated update signals to model parameters at every iteration. This work introduces a spatiotemporal view of parameter updating and proposes Event-Driven Parameter Dynamics (EPD), which separates candidate-update generation from their application through an intermediate state with accumulation, event-triggered release, and reset. In a canonical coordinate-wise instantiation, accumulation produces temporal integration, release imposes a spatial budget, and reset separates successive inter-release intervals, while conventional dense updating is recovered as a boundary case. Across four model-optimizer configurations on CIFAR-100, EPD maintains final performance close to conventional training even when only a small fraction of coordinates are released at each step, including the 1% regime. Matched-control experiments further show that temporal accumulation, state-dependent selection, and post-release reset each contribute to the observed behavior. Together, these results identify parameter-update realization as a controllable dimension of optimization and provide a concrete basis for studying its temporal and spatial organization separately from candidate-update generation.