New retina model may eventually predict outcome of eye diseases - Kogonuso


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Jun 28, 2018

New retina model may eventually predict outcome of eye diseases

A team of scientists has developed a new model of the retina that can predict with high precision the outcome of a defined perturbation, which serves as an important step toward a computer model of the retina that can predict the outcome of retinal diseases.

As machine learning and artificial intelligence develop, eye diseases will soon be described in terms of perturbations of computations performed by the retina.

The new study, published this month in Neuron, addresses whether there is enough knowledge of retinal circuits to understand how a perturbation will affect the computations a retina performs.

Understanding how the retina transforms images from the outside world into signals that the brain can interpret would provide insight into brain computations and could be useful for medicine.

An international team of scientists conducted a set of experiments combining genetics, viral and molecular tools, high-density microelectrode arrays and computer models. They perturbed a specific retinal circuit element while studying how the perturbation changed the functional properties of the different retinal output channels.

The lead author of the paper, Antonia Drinnenberg, developed a method to control the activity of horizontal cells. These are a retinal circuit element that provides feedback inhibition at the first visual synapse between photoreceptors and bipolar cells.

Using a specific set of viruses, transgenic mice and engineered ligand-gated ion channels, she was able to switch the feedback at the first visual synapse on and off. She then measured the effects of the perturbation in the retinal output using density microelectrode arrays and recorded the electrical signals of hundreds of ganglion cells simultaneously.

Surprisingly, the perturbation caused a large set of different changes in the output of the retina, she said.

"We were astonished by the variety of effects that we observed due to the perturbation of a single, well-defined circuit element," Drinnenberg said. "At first, we suspected that technical issues might underlie this variety."

After measuring the signals in thousands of ganglion cells and in defined retinal output channels, the team discovered that the variety in the horizontal cell contributions that were measured arose from the specific architecture of the retinal circuitry.

Members of the team then built the computer model of the retina, which simulated different pathways the signal could take through the retina. This enabled the team to investigate whether current understandings of the retinal circuitry could account for the effects they observed during the experiments.

They found that the model made five further predictions about the role of horizontal cells, which they had not previously seen in the data.

Horizontal cells shape the response dynamics of ganglion cells in six distinct ways, and fulfill distinct functions for different ganglion cell types. Functions include sharpening and scaling responses and enabling delayed responses.

The computational model explains the observed array of horizontal cell functions.

"One way to test our understanding of the retina is to perturb one of its elements, measure all the outputs, and see if our 'understanding,' which is a model, can predict the observed changes," said Rava A. da Silveira, another senior author on the paper. "The next step is to use the model to predict the outcome of eye diseases."

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