Computers are often thought of as artificial brains. That said, they’re nowhere close to being as complex or energy-efficient as human brains. AI consumes an enormous amount of electricity, and it’s constantly demanding more as it keeps endlessly morphing and advancing. How can our power supply catch up to a future of neural networks and digitized intelligence? Well, maybe the answer lies in merging living brain cells with a programmable electronic system.
Previous attempts to use actual neurons as the brain of a computer have run into problems. 2D neural cultures—in which the flattened neurons showed abnormal interactions and gene expression—couldn’t survive for long, and these structures were ultimately unable to replicate the connections and activity that occur in vivo. More advanced in vitro neural networks have tried to compensate for some of those problems by mirroring the structure and function of the brain with organoids. Despite some improvements, brain organoids (clumps of stem cells engineered to turn into neurons) are inconsistent and prone to both hypoxia and necrosis.
Alternative 3D neural networks known as biological neural networks (BNNs) could still be a viable option. Such a system would ideally take the form of an in vitro model that reconstructs the brain’s networks, can be reproduced, and actually lasts. It would also feature both dense and sparse neural connections (not unlike those in the hippocampus) to prevent too much data from moving around at once. In an effort to create a fusion of biology and machinery, researchers Tian-Ming Fu, James Sturm, and Kumar Mritunjay from Princeton University used electrodes and microscopic metal wires to create a 3D polymer mesh scaffold flexible enough for tens of thousands of living neurons to grow into a network that could operate with minimal energy.
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