Seeking AGI with artificial neurons that behave like real brain cells

Creating a future for AI. Image by © Tim Sandle

Modern artificial intelligence systems, from robotic surgery to high-frequency trading, rely on processing streams of raw data in real time. Extracting important features quickly is critical, but conventional digital processors are hitting physical limits. Traditional electronics can no longer reduce latency or increase throughput enough to keep up with today’s data-heavy applications. This means researchers are turning to new, innovative processes, considering processes that may also achieve artificial general intelligence (AGI)

Artificial neurons that think like real brains could unlock the next leap toward true intelligent machines. This is the conclusion of scientists from University of Southern California, who have built artificial neurons that replicate real brain processes using ion-based diffusive memristors.

These devices (neuromorphic computing) emulate how neurons use chemicals to transmit and process signals, offering massive energy and size advantages. The technology may enable brain-like, hardware-based learning systems: transforming AI into something closer to natural intelligence.

Neuromorphic engineering emulates the brain’s structure and operations, focusing on the analog nature of biological computation and the role of neurons in cognition.

Biological computing

Unlike digital processors or earlier neuromorphic chips that only simulate brain activity through mathematical models, these new neurons physically reproduce how real neurons operate. Just as natural brain activity is triggered by chemical signals, these artificial constructs use actual chemical interactions to start computational processes. This means they are not just symbolic representations but tangible recreations of biological function.

The researchers developed a device called a “diffusive memristor.” They explain how these components could lead to a new generation of chips that both complement and enhance traditional silicon-based electronics. While silicon systems rely on electrons to perform computations, diffusive memristors use the motion of atoms instead, creating a process that more closely resembles how biological neurons transmit information.

In studies, the researchers used silver ions embedded in oxide materials to generate electrical pulses that mimic natural brain functions. These include fundamental processes like learning, movement, and planning. Silver was used because it is easy to diffuse and gives the dynamics needed to emulate the biosystem, enabling the function of the neurons, with a very simple structure, to be achieved.

Future state

The diffusive memristors are efficient in both energy and size. This advancement could shrink chip sizes by orders of magnitude, cut energy use dramatically, and push artificial intelligence closer to achieving artificial general intelligence.

The research appears in the journal Nature Electronics, titled “A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor.”

Artificial neurons for more energy-efficient computing

In related research, scientists based at the University of Massachusetts Amherst have created low-voltage artificial neurons using bacteria-grown protein nanowires, enabling direct communication with biological systems.

The researchers state this discovery could lead to bio-inspired computers and wearable electronics that no longer need power-hungry amplifiers. Future applications may include sensors powered by sweat or devices that harvest electricity from thin air.