The designers used traditional transistors, but instead of using digital logic, they used them as analog circuits. To mimic the functions of the human brain (albeit on a drastically reduced scale), the researchers emulated all neural elements (except the soma) with shared electronic circuits — a design decision that maximized the number of synaptic connections. To maximize energy efficiency, the researchers used analog circuits. And to maximize throughput, they interconnected the neural arrays in a tree network.

It's considered the most cost-effective way to simulate neurons. But at $40,000 a piece, the researchers are going to have to figure out a way to drive the costs down.

Miniaturization, Autonomy, Power

Ramped-up and refined versions of this technology could be put to good use. In addition to improving our understanding of how the human brain works, it could be used to interpret signals from the brain and, in real time, use those signals to drive prosthetic limbs for paralyzed people.


These chips could also be used in robotics. A robot implanted with a Neurocore-like chip wouldn't have to be tethered to a power supply, thus increasing its autonomy.

Read the entire study at Proceedings of the IEEE: "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations." Supplemental information via Stanford.


Image: Kurt Hickman/Stanford.

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