With Brainoware, Guo aimed to use actual brain cells to send and acquire information. When the researchers applied electrical stimulation to the hybrid system they’d built, Brainoware responded to those signals, and changes occurred in its neural networks. According to the researchers, this result suggests that the hybrid system did process information, and could perhaps even perform computing tasks without supervision.
Guo and his colleagues then attempted to see if Brainoware could perform any useful tasks. In one assess, they used Brainoware to try to overcome mathematical equations. They also gave it a benchmark assess for speech recognition, using 240 audio clips of eight people pronouncing Japanese vowels. The clips were converted into electrical signals and applied to the Brainoware system. This generated signals in the neural networks of the brain organoid, which were then fed into an AI tool for decoding.
The researchers found that the brain organoid–AI system could decode the signals from the audio recordings, which is a form of speech recognition, says Guo. “But the accuracy was low,” he says. Although the system improved with training, reaching an accuracy of about 78%, it was still less accurate than artificial neural networks, according to the research.
Lena Smirnova, an assistant professor of public health at Johns Hopkins University, points out that brain organoids do not have the ability to truly hear speech but simply exhibit “a reaction” to pulses of electrical stimulation from the audio clips. And the research did not show whether Brainoware can process and store information over the long term or learn multiple tasks. Generating brain cell cultures in a lab and maintaining them long enough to perform computations is also a huge undertaking.
Still, she adds, “it’s a really good demonstration that shows the capabilities of brain organoids.”
