Abstract

The simple cells of primary visual cortex have been proposed to result from Hebbian associative learning. A recent model of this theory¹ develops a "column" of units supported by a small patch of visual field. The receptive fields of these units converge to the eigenvectors of the covariance matrix of the retinal inputs. Since many eigenvalues are effectively the same, columns associated with different patches would not in general have units with similar receptive fields in corresponding positions in the columns. A translation-invariance learning algorithm² can ensure that a layer of linear receptive fields is uniform. It can force the corresponding column elements to have the same receptive field. Simulations of these two processes on regularly sampled arrays show that they can cooperatively find eigenvectors and force corresponding column elements to have the same receptive fields. When the image is sampled at slightly disordered positions, the eigenvectors of different columns no longer are identical, but the two processes still cooperate to give an appropriate output network.

© 1992 Optical Society of America