Princeton researchers develop a 3D bio-electronic device with 70,000 living neurons that can recognize simple patterns, marking a major step toward biological computing.

In a breakthrough that blurs the line between biology and technology, scientists at Princeton University have developed a three-dimensional computing device that combines living brain cells with electronics, creating what could be an important leap toward a new generation of biological computers.
The device, described in a study published in Nature Electronics, uses real biological neurons integrated directly into an electronic system, allowing it to identify and recognize simple patterns through computational programming.

Unlike conventional machines, this is not entirely a device in the traditional sense. It is part living system, part engineered technology.
Scientists have previously explored the idea of using brain cells for computation, but earlier attempts struggled because the biological and electronic components operated separately. Most relied either on flat neuron cultures grown in petri dishes or on 3D clusters that could only be stimulated and monitored externally, limiting their potential.
The Princeton team adopted a radically different design by embedding electronics directly within the neural network.
Using advanced fabrication techniques, researchers created a 3D scaffold made of microscopic metal wires and electrodes, coated with a soft, flexible material compatible with living tissue. This framework enabled nearly 70,000 biological neurons to grow into and around the structure, forming an interconnected biological-electronic network.
The system allows scientists to both record signals from the neurons and stimulate them in real time through dozens of microscopic electrodes, creating a dynamic two-way communication between living cells and machines.
The project was led by researchers including Fu and James Sturm, alongside Kumar Mritunjay, who conducted much of the work during his graduate studies.
So far, the system has demonstrated the ability to recognize relatively simple patterns under controlled laboratory conditions. Researchers say the long-term goal is far more ambitious: refining the platform to eventually tackle increasingly complex computational tasks.
If successful, the work could open the door to a future where living neurons power machines in ways traditional silicon never could.

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