Several computational theories of early visual processing, such as Marr's zero‐crossing theory, are biologically motivated and based largely on the well‐known difference of Gaussians (DOG) receptive‐field model of retinal processing. We examine the physiological relevance of the DOG, particularly in the light of evidence indicating significant spatiotemporal inseparability in the behaviour of retinal cell types. From the form of the inseparability we find that commonly accepted functional interpretations of retinal processing based on the DOG, such as the Laplacian of a Gaussian and zero crossings, are not valid for time‐varying images. In contrast to current machine‐vision approaches, which attempt to separate form and motion information at an early stage, it appears that this is not the case in biological systems. It is further shown that the qualitative form of this inseparability provides a convenient precursor to the extraction of both form and motion information. We show the construction of efficient mechanisms for the extraction of orientation and two‐dimensional normal velocity through the use of a hierarchical computational framework. The resultant mechanisms are well localized in space‐time and can be easily tuned to various degrees of orientation and spee