I show that when a sparse, independent code is sought for time-varying natural images, the basis functions that emerge resemble the receptive field properties of cortical simple-cells in both space and time.  Moreover, the model yields a representation of time-varying images in terms of sparse, spike-like events.  It is suggested that the spike trains of sensory neurons essentially serve as a sparse code in time, which in turn forms a more efficient and meaningful representation of image structure.  Thus, a single principle may be able to account for both the receptive properties of neurons and the spiking nature of neural activity.