A group of scientists have shown that brain organoids can pass on signals that correspond to Braille letters, an essential step towards developing the energy-efficient biohybrid computers of the future. The brain organoids, remotely connected to a sensor that recorded tactile signals from different Braille letters, produced signals that were different for each letter, allowing a decoder to distinguish between the different letters the organoids had been given. Although individual brain organoids provided accurately decodable signals 61% of the time, a team of three performed significantly better, producing an accurate signal 83% of the time. The team of three was also better at distinguishing the signals from artificial noise than the decoder working directly on the obscured signals, without the organoids’ involvement.

Could computers include tiny brain organoids one day? The human brain is an incredibly energy-efficient computer, and silicon processors that try to imitate it achieve huge energy savings over traditional processors. Now scientists are experimenting with using organoids modelled on the human forebrain to develop energy-efficient biohybrid computing. A crucial first step towards this is learning whether the organoids can receive and relay signals reliably, which a Bristol-based team of researchers investigated using Braille letters. They found that teams of three brain organoids produced signals which accurately corresponded to different letters 83% of the time.

“We can take information from a real tactile sensor, turn it into a form that a living neural network can receive, and then recover meaningful information from the organoid response,” said Tianyi Liu, PhD candidate at Bristol University and first author of the article in Frontiers in Artificial Intelligence. “That gives us a practical way to connect artificial sensors with biological neural systems, which are a very interesting possible route towards more energy-efficient computing.”

From A to Braille

The biohybrid systems tested by Liu and her team use brain organoids placed on multielectrode arrays and contained in a system that keeps the organoids alive and monitors their responses. These organoids are less than a millimeter across and lack several structures that a human brain has, but contain about 10,000 neurons, as well as supporting cells. The multielectrode arrays allow them to receive electrical signals.

First, the scientists applied a sensor that records tactile data to the 26 letters of the Braille alphabet. Then they converted this data to electrical stimulation patterns and presented the organoids with the different patterns to see if they would produce neural activity that could tell the scientists what stimulus they’d received. In other words, were they able to ‘read’ the electrical signal that corresponded to each Braille letter, and produce a signal that recognizably corresponded to the information they’d been given?

The organoids’ patterns of neural activity were classified using a simple machine learning algorithm, a support vector machine, that could sort the patterns and detect whether given signals corresponded to specific letters. A single organoid consistently produced a signal that could be accurately decoded as a letter about 61% of the time. However, three organoids working together produced a recognizable signal about 83% of the time.

“After processing by the organoids, the tactile data still contains enough information to classify Braille letters with good accuracy,” said Dr Benjamin Ward-Cherrier of Bristol University, senior author of the article. “Combining outputs from several organoids also showed an improvement in performance.”

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Three brains are better than one?

The researchers can’t yet tell why three organoids performed better than just one; it could be because individual variation between the organoids means that they complement each other.

“Different organoids may have slightly different network connections, developmental states, and ongoing activity, so the same input can produce similar but not identical responses,” said Liu. “Combining those responses may give us complementary information that we would not get from a single organoid. At the same time, if every organoid behaves differently, it obviously makes it harder to build a predictable and standardized system.”

To make the task harder, the scientists applied different noise filters to the Braille letter signals. They also used the same support vector machine to compare decoding from the organoid responses with direct decoding of the deliberately obscured signals. The machine decoding directly, without the organoids’ involvement, turned out to be easier to fool than decoding from the organoid responses — it was less able to identify the letter signals among the artificial noise.

Closing the loop

The scientists stress that these experiments don’t show if organoids can learn over time; they just show that the organoids received and responded to the Braille signals.

“We showed that tactile information could be encoded, processed through the organoids, and decoded, but we did not demonstrate learning or long-term changes in the organoids,” said Liu. “We also tested one tactile task using a fixed encoding scheme and a relatively simple measure of neural activity. The next step is definitely to move beyond Braille; we are interested in things such as textures, edges, and different kinds of touch, and eventually in closed-loop tasks where the organoid receives feedback from what it does. That would allow us to start asking whether the organoid can actually adapt through experience.” 

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