Brain implants to revive vision, akin to Neuralink's Blindsight, face a fundamental problem: more pixels don’t lead to higher vision

Elon Musk recently pronounced The next Neuralink project shall be a “Blindsight” cortex implant to revive vision: “The resolution will initially be low, like the early Nintendo graphics, but could ultimately go beyond normal human vision.”

Unfortunately, this claim relies on the Misconception that neurons within the brain are like pixels on a screen. Not surprisingly, engineers often assume that “more pixels mean better vision.” After all, that’s exactly how monitors and phone screens work.

In our newly published study, we now have Computer model of human vision to simulate what type of vision a particularly high-resolution cortical implant might provide. A movie of a cat at 45,000 pixels is sharp and clear. In a movie made with a simplified version of a model fabricated from 45,000 cortical electrodes, each stimulating a single neuron, a cat remains to be recognizable, but a lot of the scene's details are lost.

The reason why the film produced by electrodes is so blurry is because neurons within the human visual cortex should not tiny dots or pixels. Instead, each neuron has a selected receptive fieldthat’s, the position and pattern that a visible stimulus will need to have to activate that neuron. Electrical stimulation of a single neuron produces a blob whose appearance is decided by that neuron's receptive field. The smallest electrode – one which stimulates a single neuron – produces a blob that’s in regards to the width of your little finger when held at arm's length.

Consider what happens if you take a look at a single star within the night sky. Each point in space is represented by many hundreds of neurons with overlapping receptive fields. A tiny point of sunshine, akin to a star, results in a posh pattern of activations in all of those neurons.

To create the visual experience of seeing a single star by stimulating the brain, you would need to reproduce a pattern of neural responses just like the pattern that might occur during natural vision.

Of course, this could require hundreds of electrodes. But you’d also must recreate the right pattern of neuronal responses, which requires knowing the receptive field of every neuron. Our simulations show that it will not be enough to know the position of every neuron's receptive field in space – in case you don't also know the orientation and size of every receptive field, the star becomes a blurry mess.

Even a single star—a single vivid pixel—produces an immensely complex neural response within the visual cortex. Imagine the much more complex pattern of cortical stimulation required to accurately reproduce natural vision.

Some scientists have suggested that by stimulating the correct electrode combinationwould it not be possible, create natural visionUnfortunately, nobody has yet proposed a meaningful method to find out the receptive field of every neuron in a given blind patient. Without this information, there isn’t a technique to see the celebs. Vision from cortical implants stays grainy and imperfect, whatever the variety of electrodes.

Restoring vision will not be simply a technical problem. To predict what type of vision a tool will provide, one must know the way the Technology interacts with the complexity of the human brain.

This is how we created our virtual patients

In our work as mathematically Neuroscientistwe develop simulations that predict the perceptual experience of patients who need to restore their vision.

We have already developed a model to predict the Perceptual experience of patients with a retinal implantTo create a virtual patient that predicts what patients with cortical implants would see, we simulated the neurophysiological architecture of the world of ​​the brain involved in first stage of visual processingOur model approximates how the scale of receptive fields increases from central to peripheral vision and takes into consideration the indisputable fact that each neuron has a singular receptive field.

Our model has successfully predicted Data describing participants' perceptual experience from a wide range of cortical stimulation studies in humans. After confirming that our model could predict existing data, we used it to make predictions in regards to the quality of vision that potential future cortical implants could produce.

Models like ours are an example of Virtual prototypingwhich uses computer systems to enhance product design. These models can facilitate the event of recent technologies and evaluate the performance of devices. Our study shows that they may provide more realistic expectations about what type of vision bionic eyes might provide.

First, do no harm

In our nearly 20 years of research into bionic eyes, we now have seen the complexity of the human brain overwhelm one company after one other. The costs are borne by the patients When these devices fail, they’re left with orphaned technology of their eyes or brains.

The Food and Drug Administration could require vision restoration firms Failure plans that minimize damage for patients when technologies not work. One of the probabilities is that firms that implant neuroelectronic devices in patients are required to Technology trust agreements and take out insurance to make sure continuous medical care And Technology Support in the event that they go bankrupt.

If cortical implants may even come near the resolution of our simulations, it could still be an achievement price celebrating. Grainy and imperfect vision would change the lives of hundreds of individuals currently affected by incurable blindness. But now’s a moment for caution, not blind optimism.