Princeton researchers have built a 3D device that combines living brain cells with advanced electronics in one system.
The device uses computational methods to recognize electrical patterns and may help researchers study brain function, neurological disease, and low-power computing.
Earlier attempts to use brain cells for computation usually relied on flat 2D cell cultures grown in petri dishes or 3D cell clusters that researchers monitored and stimulated from the outside.
The Princeton system takes a different approach. It interacts with the cells from inside the network.
The team used advanced fabrication methods to build a 3D mesh of microscopic metal wires and electrodes. A very thin epoxy coating holds the mesh together and remains flexible enough to work with the soft neurons growing around it.
Researchers used the mesh as a scaffold, allowing tens of thousands of neurons to grow into a large 3D network capable of computation.
The chip includes around 70,000 biological neurons connected through the 3D mesh. It also has dozens of microscopic electrodes that can sense and control the activity of the brain cells.
The researchers said the integrated design allowed them to record and stimulate neuronal electrical activity with much finer detail than earlier systems.
Over more than six months, they tracked how the network changed. They also tested ways to strengthen or weaken connections between important neurons.
The team then trained an algorithm to identify patterns in electrical pulses.
In one experiment, the system was tested with pairs of different spatial patterns. In another, it was tested with different temporal patterns. In both cases, the system correctly distinguished between the patterns.
The researchers said they now want to expand the platform so it can eventually handle more complex tasks.
The work was led by Tian Ming Fu, assistant professor of Electrical and Computer Engineering and the Omenn Darling Bioengineering Institute; James Sturm, Stephen R. Forrest Professor of Electrical and Computer Engineering; and Kumar Mritunjay, a postdoctoral researcher in electrical and computer engineering.
Mritunjay was the paper’s first author and was a graduate student when most of the work was completed.
The project was first developed to study basic questions in neuroscience. However, the team later saw that it could also help address one of the biggest challenges in modern AI: energy use.
“The real bottleneck for AI in the near future is energy,” Fu said. “Our brain consumes only a tiny fraction, about one millionth, of the power consumed by today’s AI systems to perform similar tasks.”
Mritunjay said systems like this, called 3D biological neural networks, “not only help uncover the computing secrets of the brain but can also assist in understanding and possibly treating neurological diseases.”
Image credits in the source were listed as Wright Señeres and Courtesy of the researchers/Princeton University.
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