Scientists invent ‘Brainoware’ computer that uses human neurons and technology hardware

  • Human brain organoids integrate electronic circuits to solve problems
  • This hybrid is a form of “reservoir computing” that utilizes brain cells
  • Read more: A “thinking” robot with neurons grown in the lab from living brain cells

Scientists have unveiled a hybrid computer called “Brainnoware” made of electronics and tissue similar to the human brain. This is part of a growing field called biocomputing.

The new technology features brain “organoids” made of human stem cells sitting on circuit boards that feed the organoids with information and read their responses.

This bio-electronic hybrid was able to identify people’s voices and make predictions about complex math problems.

Researchers say the discovery represents an important step toward hybrid computers that blend humans and machines to perform complex computing problems using a fraction of the power required by traditional computers. claims.

The human brain organoid rests on a microelectrode array that feeds information into tiny chunks of tissue and records its responses.

The scientists who developed Brainoware say it could also support artificial intelligence by allowing computers to imitate the human brain.

The human brain is a natural computer that evolved over millions of years. And even as electronic computers become more sophisticated and powerful, the brain’s efficiency remains superior when it comes to power requirements, writes the Brainoware team. “The human brain normally consumes about 20 watts, but his current AI hardware consumes about 8 million watts” Comparison[人工ニューラルネットワーク]Drive.[artificialneuralnetwork'[artificialneuralnetwork’

In the new Brainoware computer, brain “organoids” made from human stem cells sit on circuit boards that feed information to the organoids and read their responses.

Brainoware is an example of what computer scientists call “reservoir computing,” in which a computer feeds information into a complex network, in this case a brain organoid, and interprets its output.

The idea behind reservoir computing is as follows. By sending information to brain tissue and reading its output, computers can take advantage of the complexity of the organoids and train and adjust them, without requiring a complete map or understanding of cellular networks.

In other words, an organoid is a kind of “black box” and scientists don’t need to know exactly how it works in order to use it.

The organoids are not part of a living human brain, but grow from so-called pluripotent stem cells, cells that can be induced to form all kinds of body tissues.

Scientists at Indiana University Bloomington, the University of Florida, and Cincinnati Children’s Hospital Medical Center used pluripotent stem cells to grow human cortical organoids.

Brain organoids grow over several months. In the first week, “induction” occurs, where scientists guide stem cells to grow into brain cells. Over the course of two weeks, these neural progenitor cells divide and further increase in number. After 1 month, the organoids contain multiple types of brain cells, and after 2 months, the organoids are mature

After about a month of growth, the organoids contained several types of human brain cells, including neurons, neural progenitor cells that grow into various brain cells, and immune cells called astrocytes.

These organoids mimic very basic aspects of the human cerebral cortex, the surface of the brain that performs important functions such as learning, reasoning, and problem solving.

In a speech recognition experiment, Brainnoware was asked to recognize various Japanese male voices from a collection of 240 audio clips.

In the first test, we were only able to achieve an accuracy of about 51%.

However, after several days of training, its performance improved to 78% accuracy.

The Brainoware computer setup (left) consists of cortical organoids sitting on top of a microelectrode array. The organoids used in Brainoware contain multiple types of brain cells, including mature neurons and astrocytes (top right) and early stage neurons and neural progenitor cells (bottom right).

“These results indicate that electrical stimulation during training can trigger unsupervised learning in brainnoware that improves computing performance by reshaping the functional connectivity of organoids.” the study authors wrote.

Although organoids cannot “think” and do not have consciousness as we know it, they have a natural ability to form new connections.

A major part of what makes new computers so powerful is the human brain’s ability to reconfigure itself.

Under normal circumstances, the human brain forms new connections in response to new experiences, forming memories and learning skills that are stored in connections between neurons.

And that seems to be what’s happening with Brainoware.

In another test of problem-solving ability, scientists tasked Brainnoware with predicting Henon maps, chaotic nonlinear mathematical equations.

Similar to the speech recognition exercise, the system went through several training systems to improve its accuracy.

After three months of growth, the brain organoids contained not only mature neurons but also neural progenitor cells that can develop into neurons.

When scientists blocked neuroplasticity, the ability of organoids to form new connections, no such improvements occurred.

“The results showed that Brainoware’s learning activity relies on neuroplasticity,” they wrote.

The work was published in today’s magazine nature electronics.

Brainoware’s development supports the idea that these brain-hybrid computer systems can solve complex problems, but more research is needed to actually prove their capabilities, says another team of researchers. I am writing this with an explanation.

“The next step to show that brain organoids can serve as an in vitro learning model will require demonstrating aspects of long-term memory, the ability to learn multiple tasks, and the ability to distinguish the unique contributions of brain organoids.” “Connected computing systems,” they write.

“This is important for studying the biological mechanisms that enable continuous and lifelong learning.”

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