Princeton Scientists Develop 3D Computing Device by Integrating Living Brain Cells and Electronics

Scientists at Princeton University in the United States have ingeniously integrated living brain cells with cutting-edge electronic technology to create a 3D computing device that can be programmed to recognize different patterns. The findings, detailing this innovative integration, have been published in the latest issue of Nature Electronics.

The research team employed advanced manufacturing techniques to weave a 3D grid using fine metal wires and electrodes, which is gently supported by an ultra-thin epoxy coating—so thin that it grants the grid just the right flexibility to “closely” connect with the soft neurons growing around it. Taking this grid as the framework, the team cultivated tens of thousands of neurons into a large-scale three-dimensional network, which is used to perform computing tasks.

The new integration method enables the team to record and regulate the electrical activity of neurons at an unprecedentedly fine scale. Over a period of more than six months, they continuously tracked the evolution of the system, attempting to strengthen or weaken the connections between key neurons, and ultimately trained an algorithm capable of accurately identifying electrical pulse patterns.

In one test, the system was presented with different paired spatial patterns; in another, it distinguished between various temporal patterns. After both rounds of tests, the system made precise distinctions in each case. The team noted that they aim to further expand the system in the future to take on increasingly complex tasks.

This newly developed system is known as a “3D biological neural network”. The Princeton research team emphasized that it not only opens a window to unravel the mysteries of brain computing but also holds great promise for helping people understand and even treat neurological diseases. According to the research paper, the 3D structure of the device mimics the natural environment of brain neurons, allowing for more realistic and efficient information processing compared to traditional flat computing models.

Neuroscience experts commented that this breakthrough bridges the gap between biological systems and electronic technology, offering a new direction for the development of next-generation computing devices. Unlike conventional electronic computers that rely on silicon chips, the 3D biological neural network leverages the inherent computing capabilities of living neurons, which are more energy-efficient and adaptable to complex tasks.

The research team, led by Dr. Sarah Johnson from Princeton University’s Department of Electrical and Computer Engineering, stated that the device’s ability to learn and adapt through the regulation of neuron connections closely resembles the learning process of the human brain. This similarity, they added, makes the system particularly valuable for studying brain function and developing new treatments for conditions such as Alzheimer’s and Parkinson’s disease.