Dr. Botond Roska, a postdoctoral fellow from Hungary, measured output of retinal cells. Robert Sanders photo

Over a period of nearly three years, Roska painstakingly measured signals from more than 200 ganglion cells in the rabbit retina as he flashed pictures of a featureless square or circle. Ganglion cells are the eye's output cells, forming the optic nerve connecting it to the brain.

"We made very simple measurements on retinal cells, recording excitation and spiking when we flashed squares and moving spots in front of the eye," Roska said.

From these, he and Werblin determined that there are about a dozen different populations of ganglion cells, each spanning the full visual space and producing a different movie output.

One group of ganglion cells, for example, only sends signals when it detects a moving edge. Another group fires only after a stimulus stops. Another sees large uniform areas, yet another only the area surrounding a figure.

"Each representation emphasizes a different feature of the visual world — an edge, a blob, movement — and sends the information along different paths to the brain," Werblin said.

Robert Sanders photo

The two researchers shared these detailed findings with software designer David Balya in Hungary, who modeled the visual processing on a computer, a preliminary step before actually programming a CNN chip to simulate the image processing that goes on in the eye. The computer model precisely mimics the output of the ganglion cells of the retina, vividly showing the difference between the world we see and the information that actually is sent to the brain.

Listen to Frank Werblin (right) explaining the language of cell interactions (audio with photo).

"We now are looking at the predictions the model makes when viewing natural scenes — Frank's face or leaves on the ground — and comparing them with what we measure in actual retinal cells, to learn how good these predictions are," Roska said.

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