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Learning the Gestalt Rule of Collinearity from Object Motion
2003
Neural Computation
The Gestalt principle of collinearity (and curvilinearity) is widely regarded as being mediated by the long-range connection structure in primary visual cortex. We review the neurophysiological and psychophysical literature to argue that these connections are developed from visual experience after birth, relying on coherent object motion. We then present a neural network model that learns these connections in an unsupervised Hebbian fashion with input from real camera sequences. The model uses
doi:10.1162/08997660360675071
pmid:14511516
fatcat:nf6prvdl6bgh7kbv2l5hj7nqi4