Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates signals of activation distributed throughout the broader transcriptome to estimate neuronal activation in a way that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published single cell and time course studies of activity-induced gene expression. Further, using spatial transcriptomic techniques, we demonstrate the model's ability to identify patterns of learning-induced activation. In conclusion, NEUROeSTIMator is a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.