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.