Scientists have made a breakthrough toward the mind-bending goal of creating computers made of brains by demonstrating that human neural tissue can classify letters of the Braille alphabet, according to a study published on Friday in Frontiers in Artificial Intelligence.
A team transmitted electrical signals corresponding to Braille letters to human brain organoids, which are living neural tissues derived from stem cells. The results revealed that a single organoid, consisting of about 10,000 neurons, could classify the letters with about 61 percent accuracy, while teams of three organoids reached 83 percent accuracy.
“The human brain can perform incredibly complex information processing while consuming relatively little energy, and artificial neural networks were originally inspired by biological nervous systems,” said Tianyi Liu, a PhD candidate at Bristol University who led the new study, in an email to 404 Media.
“If we've been trying to imitate biological neural networks all along, why not try using real biological neural networks directly?” she continued. “What kind of information processing capabilities might these tiny neural organoids have? Could they show some form of learning or adaptation? These are all questions we're very interested in.”
The new study falls under the emerging field of organoid intelligence, which aims to use organoids as biological “wetware” for computing applications. Previous studies have established that organoids are capable of recognizing speech Japanese vowels or even playing the classic game Pong.
Since Liu and her team specialize in how robots sense and respond to touch, they homed in on Braille as a way to convert tactile information into electrical stimuli that an organoid could receive, process, and classify.
In their experimental setup, the researchers used a tactile sensor to scan data from the 26 letters of the Braille alphabet, which is built from arrangements of raised dots. This data was converted into electrical stimulation patterns that were fed to the organoids, which are smaller than a millimeter in scale and positioned on arrays that keep them alive.
“Our original goal wasn't to prove that organoids could read Braille like humans, or that they were already ‘intelligent’ enough to understand Braille,” she added. “Rather, we wanted to use this task to explore whether real biological neural networks could participate in processing information from robotic tactile sensors.”
In other words, the researchers aimed to discover if these stimulated inputs could be decoded by the organoids and returned back in discernable responses corresponding to the Braille letters. The results revealed that a single organoid could accurately classify the letters nearly two thirds of the time, while an "ensemble" of three organoids performed even better, with an accuracy of 83 percent.
“We still don't know the exact mechanism behind this improvement,” Liu said. “Our hypothesis is that different organoids respond to the same stimulation in similar, but not identical, ways.”
She added that “you can think of it as three people looking at the same object. Although they're all looking at the same thing, each person might notice different details. Some features might not be obvious to one person but could be quite easy for another to spot. By combining their observations, you might be able to make a more accurate and reliable judgement.”
The results are undeniably trippy, conjuring up the classic scifi trope of a hyperintelligent brain-in-a-vat. But Liu emphasized that this was an open-loop experiment, meaning that the organoids only received initial inputs without getting new feedback about their performance on the tasks. For this reason, the study does not prove that the organoids actually learned Braille, or were altered in any permanent way, just that they could classify Braille letters after receiving the electrical inputs.
“In the future, if we can build closed-loop systems where neural activity from the organoids actually participates in a robot's perception, decision making, and control, while the results of the robot's interactions with its environment are fed back to the organoids, we could begin to investigate whether these biological neural networks can adapt to new tasks through ongoing interaction,” she said.
Liu also noted that while there are many potential advantages to organoid intelligence, there are plenty of limitations to biological computing systems. For example, organoids cannot be standardized, leading to natural variations that can complicate results, plus they have to be kept alive in cultures that are more difficult to maintain than electronic devices.
“Compared with established computing chips, organoid systems still have a long way to go in terms of reliability, reproducibility, and scalability,” Liu said.
Even so, organoid intelligence “gives us the opportunity to explore a way of computing that's very different from traditional computers,” she concluded.