In Section 1 of this chapter we review the existing models of peripheral vision, most of which are based on the appealing conception that peripheral vision is just a spatially scaled version of central vision. We argue against scaling models, however, on the grounds that they cannot account for the primary factor which limits resolving power in the periphery: neural undersampling of the optical image formed on the retina. In order to account for sarnpling effects, we adopt an engineering perspective to develop in Section 2 a simple model of optical and neural processing of the retinal image. In Section 3, we apply our neuro-optical model to human eyes in order to discover the relative importance of the optical and neural limits to pattern detection and resolution. The results show that, although the optical system of the eye is the dominant factor limiting central vision, spatial undersampling by the optic nerve cells of the retina limits resolution in the periphery. Neural undersampling of the retinal image leads to perceptual aliasing and spurious detection of patterns up to an order of magnitude finer than the Nyquist limit. Aliasing is curtailed at very high frequencies by a combination of optical filtering and spatial summation over the finite aperture of cone photoreceptors. We conclude with some comments on practical applications of the model.