Independent component analysis of temporal sequences subject to constraints by lateral geniculate nucleus inputs yields all the three major cell types of the primary visual cortex.

Information maximization has long been suggested as the underlying coding strategy of the primary visual cortex (V1). Grouping image sequences into blocks has been shown by others to improve agreement between experiments and theory. We have studied the effect of temporal convolution on the formation of spatiotemporal filters--that is, the analogues of receptive fields--since this temporal feature is characteristic to the response function of lagged and non-lagged cells of the lateral geniculate nucleus. Concatenated input sequences were used to learn the linear transformation that maximizes the information transfer. Learning was accomplished by means of principal component analysis and independent component analysis. Properties of the emerging spatiotemporal filters closely resemble the three major types of V1 cells: simple cells with separable receptive field, simple cells with non-separable receptive field, and complex cells.