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Tristan Free
Human brain organoids can relay signals that correspond to Braille letters – in other words, the tiny tissue cultures can now ‘read’.
Making strides toward biohybrid computing, researchers from the University of Bristol (UK) have demonstrated human brain organoids capable of ‘reading’ Braille. The bookish ‘mini brains’ were remotely connected to a sensor that recorded signals from different letters of the Braille alphabet, which they were then able to reproduce, allowing a machine learning system to decode the letters the organoids had been given.
The human brain is an incredibly efficient computer, so much so that scientists have been attempting to harness its power to enhance traditional computing for decades. In fact, silicon-based processors designed to emulate it achieve notable energy savings when compared to conventional computing systems. But what if we could move away from imitating the brain and instead incorporate living neural tissue for information processing?
This is the basis of biohybrid computing, which has been proposed as one route to reducing the energy demands of silicon-based systems. Using brain organoids grown on multielectrode arrays, researchers are aiming to create living networks that can reorganize and learn while consuming orders of magnitude less energy. As a first step on this path, they must establish whether organoids can receive and relay signals.
To explore this potential capability, the Bristolian team first cultured human forebrain organoids on a low-density microelectrode array, which delivered electrical signals to the cells. Each organoid measured less than a millimeter across and was composed of around 10,000 neurons, as well as astrocytes and oligodendrocytes. Maintained under continuous perfusion, the mini brains were kept alive for approximately 100 days.
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The team next applied a sensor that recorded tactile data from the 26 letters of the Braille alphabet. These data were converted to electrical stimuli, which were presented to the organoids in different patterns to see if they produced decipherable neural activity in response.
A support vector machine, a type of machine learning algorithm, was then used to decode the resulting organoid activity, revealing that certain signals corresponded to specific letters. Single organoids were able to accurately classify the Braille letter 61% of the time on average; however, when three organoids were combined, this increased to 83%.
“Different organoids may have slightly different network connections, developmental states and ongoing activity, so the same input can produce similar but not identical responses. Combining those responses may give us complementary information that we would not get from a single organoid,” explained study first author Tianyi Liu.
In addition, the researchers applied different noise filters to the Braille letter signals, finding that multi-organoid systems demonstrated an improved ability to identify letter signals amongst various types of artificially introduced noise when compared to the support vector machine alone.
“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,” Liu stated. “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.”
However, the authors are keen to stress that they did not demonstrate learning or long-term changes in the mini brains, just that they could respond to stimuli in a predictable way, at least for now.
“The next step is definitely to move beyond Braille,” added Liu. “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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