Without temporal averaging, such as rate codes, it remains a challenge to precisely train spiking neural networks for temporal regression tasks. Due to the temporal dynamics in biologically plausible neural systems, training a spiking network to provide precise numerical predictions implies a trade-off between dynamics that are active and adaptive versus inactive and sluggish. Here, we present a novel method to accurately predict translation-invariant spatial coordinates from sparse, event-based vision (EBV) signals using a fully spiking convolutional neural network (SCNN) without temporal averaging.