A team at Princeton University has developed a 3D device that combines tens of thousands of living neurons with an embedded electronic mesh, constituting a biological neural network capable of recognizing intricate electrical patterns. This study, published in Nature Electronics, introduces a novel bio-hybrid computing approach that could mitigate the escalating energy needs of artificial intelligence.
The device employs an “Inside-Out Architecture,” setting it apart from earlier brain-on-chip models that depended on flat, two-dimensional cell cultures or externally probed three-dimensional structures. Researchers created a three-dimensional mesh with microscopic metal wires and electrodes coated with a flexible epoxy to interface seamlessly with soft biological tissue. Neurons were cultivated on this scaffold, forming a dense and functional network over time.
This integrated design enables researchers to stimulate and record neural activity more precisely than previous methodologies. Over six months, the team experimented with modifying neuronal connections and successfully trained an algorithm to differentiate between spatial and temporal electrical patterns. The project was led by Tian-Ming Fu, assistant professor of electrical and computer engineering, along with James Sturm, the Stephen R. Forrest Professor of Electrical and Computer Engineering, and postdoctoral researcher Kumar Mritunjay.
The research initially focused on fundamental neuroscience but revealed significant implications for AI hardware, particularly concerning energy consumption. “The real bottleneck for AI in the near future is energy,” Fu stated. “Our brain consumes only a tiny fraction — about one millionth — of the power consumed by today’s AI systems to perform similar tasks.”
This device is part of a growing trend that seeks to blur the boundaries between biological and electronic systems. Recent demonstrations by Northwestern University researchers showcased printed artificial neurons triggering responses in living mouse brain cells, while the Princeton device advances this concept by embedding electronics within the living network itself, offering higher integration and enhanced control capabilities.
Mritunjay commented that these systems “not only help uncover the computing secrets of the brain but can also assist in understanding and possibly treating neurological diseases.” The research team plans to scale the platform for more complex computational tasks, with anticipated long-term applications in neuromorphic chip design, drug testing, and brain-machine interfaces.
