Single Cell Brain Control
People can exert conscious control over individual neurons, using that control to alter images on a computer screen, according to research published this week in Nature. Researchers say the findings help explain how our brain decide which stimuli in our noisy environment to pay attention to and which to ignore. The research may ultimately aid in the development of brain computer interfaces designed to help severely paralyzed people communicate.
Christof Koch and collaborators at Caltech studied people awaiting brain surgery for epilepsy; these patients have electrodes implanted directly into their brains to record their neural activity. Previous research by the same team had shown that individual neurons can respond preferentially to images of specific objects or people, such as Hallie Berry. In the new experiment, researchers identified some of these cells and then asked patients to try to manipulate their activity. They translated the neural signals into a control signal for a nearby computer.
An article in Nature News describes the experiment:
In this experiment, the scientists flashed a series of 110 familiar images – such as pictures of Marilyn Monroe or Michael Jackson – on a screen in front of each of the 12 patients and identified individual neurons which uniquely and reliably responded to one of the images. They selected four images for which they had found responsive neurons in different parts of a subject’s MTL.
Then they showed the subject two images superimposed on each other. Each was 50% faded out.
The subjects were told to think about one of the images and enhance it. They were given ten seconds, during which time the scientists ran the firing of the relevant neurons through a decoder. They fed the decoded information back into the superimposed images, fading the image whose neuron was firing more slowly and enhancing the image whose neuron was firing more quickly.
Watching this on-line feedback, the subjects were able to make their targeted image completely visible, and entirely eliminate the distracting image, in more than two thirds of trials, and they learnt to do so very quickly.
Afterwards, they reported that they had used different cognitive strategies. Some tried to enhance the target image, while others tried to fade the distracting images. Both had worked. But feedback on the computer screens was vital. When this ‘brain-machine interface’ wasn’t provided, their success rates plummeted below one third.
The inside story of how ChatGPT was built from the people who made it
Exclusive conversations that take us behind the scenes of a cultural phenomenon.
ChatGPT is about to revolutionize the economy. We need to decide what that looks like.
New large language models will transform many jobs. Whether they will lead to widespread prosperity or not is up to us.
Sam Altman invested $180 million into a company trying to delay death
Can anti-aging breakthroughs add 10 healthy years to the human life span? The CEO of OpenAI is paying to find out.
GPT-4 is bigger and better than ChatGPT—but OpenAI won’t say why
We got a first look at the much-anticipated big new language model from OpenAI. But this time how it works is even more deeply under wraps.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.