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image in a certain part of the area is, say, colored white, while others will be positive if it is colored black. And the cell in the visual cortex will then respond only if enough of its inputs are positive, corresponding to a specific pattern being present in the image.

In practice many details of this setup are quite complicated. But as a simple idealization, one can consider an array of squares on the retina, each colored either black or white. And one can then assume that in the visual cortex there is a corresponding array of cells, with each cell receiving input from, say, a 2×2 block of squares, and following the rule that it responds whenever the colors of these squares form some particular pattern.

The pictures below show a simple example. In each case the first picture shows the image on the retina, while the second picture shows which cells respond to it. And with the specific choice of rule used here, what effectively happens is that the vertical black edges in the original image get picked out.

Neurophysiological experiments suggest that cells in the visual cortex respond to a variety of specific kinds of patterns. And as a simple idealization, the pictures on the next page show what happens with cells that respond to each of the 16 possible 2×2 arrangements of black and white squares. In each case, one can think of the results as corresponding to picking out some specific local feature in the original image.


Responses to two sample images of cells sensitive to the 2×2 template shown on the bottom left. The cells that respond are indicated by darker squares in the second picture in each pair. Such responses occur whenever the 2×2 template on the left appears, corresponding to the presence of a vertical black edge. The extraction of features by this kind of simple template matching appears to be a key element in human visual perception—as well as being common in technological image processing. The sample images used here are ones generated by the evolution of elementary one-dimensional cellular automata with rules 60 and 124 respectively.

image in a certain part of the area is, say, colored white, while others will be positive if it is colored black. And the cell in the visual cortex will then respond only if enough of its inputs are positive, corresponding to a specific pattern being present in the image.

In practice many details of this setup are quite complicated. But as a simple idealization, one can consider an array of squares on the retina, each colored either black or white. And one can then assume that in the visual cortex there is a corresponding array of cells, with each cell receiving input from, say, a 2×2 block of squares, and following the rule that it responds whenever the colors of these squares form some particular pattern.

The pictures below show a simple example. In each case the first picture shows the image on the retina, while the second picture shows which cells respond to it. And with the specific choice of rule used here, what effectively happens is that the vertical black edges in the original image get picked out.

Neurophysiological experiments suggest that cells in the visual cortex respond to a variety of specific kinds of patterns. And as a simple idealization, the pictures on the next page show what happens with cells that respond to each of the 16 possible 2×2 arrangements of black and white squares. In each case, one can think of the results as corresponding to picking out some specific local feature in the original image.


Responses to two sample images of cells sensitive to the 2×2 template shown on the bottom left. The cells that respond are indicated by darker squares in the second picture in each pair. Such responses occur whenever the 2×2 template on the left appears, corresponding to the presence of a vertical black edge. The extraction of features by this kind of simple template matching appears to be a key element in human visual perception—as well as being common in technological image processing. The sample images used here are ones generated by the evolution of elementary one-dimensional cellular automata with rules 60 and 124 respectively.

From Stephen Wolfram: A New Kind of Science [citation]