New Artificial Neurons Physically Replicate the Brain

A breakthrough in neuromorphic computing could lower the energy consumption of chips and accelerate progress toward artificial general intelligence (AGI).

Researchers from the USC Viterbi School of Engineering and the School of Advanced Computing have created artificial neurons that closely mimic the complex electrochemical behavior of real brain cells. Their breakthrough, described in Nature Electronics, represents a major step forward in neuromorphic computing. This new approach could dramatically shrink chip size, cut energy use by several orders of magnitude, and bring us closer to achieving artificial general intelligence (AGI).

Unlike standard digital processors or existing silicon-based neuromorphic chips that only simulate neural activity, these artificial neurons physically reproduce the analog processes of biological neurons. In the same way that neurochemicals trigger brain activity, specific chemicals can now be used to initiate computation in brain-inspired, or neuromorphic, hardware. Because they replicate the actual biological mechanisms rather than relying on mathematical models, these artificial neurons are fundamentally different from earlier designs.

The research, led by USC Computer and Electrical Engineering Professor Joshua Yang—who previously made pioneering contributions to the field of artificial synapses—introduces a new type of artificial neuron built using what is known as a “diffusive memristor.” The study details how this innovation could enable a new generation of chips that enhance and extend today’s silicon-based technologies. While conventional electronics depend on the flow of electrons for computation, Yang’s diffusive device instead uses the movement of atoms. This atomic-level process allows the neurons to operate more like those in the human brain, offering greater energy efficiency and the potential to advance the development of AGI.

How it works

In the biological process, the brain uses both electrical and chemical signals to drive action in the body. Neurons or nerve cells start out with electrical signals that when they reach the space or gap at the end of the neuron called the synapse, the electrical signals are converted into chemical signals into order to pass on and process the information. Once the information crosses to the next neuron, some of those signals are once again converted to electrical signals through the body of the neuron. This is the physical process that Yang and colleagues have succeeded in emulating with high fidelity in several critical aspects. The big advantage: their diffusive memristor-based artificial neuron requires only the space of a single transistor, rather than the tens to hundreds used in conventional designs.

In particular, in the biological model, ions or charged particles help generate the electrical signals to cause action within the neuron. In the human brain, such processes rely on chemicals (e.g., ions) like potassium, sodium, or calcium to force this action.

In the current paper, Yang, who is Director of the Center of Excellence on Neuromorphic Computing at USC, uses silver ions in oxide to generate the electrical pulse and emulate the processes to perform computing for activities such as movement, learning, and planning.

“Even though it’s not exactly the same ions in our artificial synapses and neurons, the physics governing the ion motion and the dynamics are very similar,” says Yang.

Yang explains, “Silver is easy to diffuse and gives us the dynamics we need to emulate the biosystem so that we can achieve the function of the neurons, with a very simple structure.” The new device that can enable a brain-like chip is called the “diffusive memristor” because of the ion motion and the dynamic diffusion that occurs with the use of silver.

He adds, the team chose to utilize ion dynamics for building artificial intelligent systems “because that is what happens in the human brain, for a good reason and since the human brain, is the ‘winner in evolution–the most efficient intelligent engine.”

“It’s more efficient,” says Yang.

This is critical, explains Yang, “It’s not that our chips or computers are not powerful enough for whatever they are doing. It’s that they aren’t efficient enough. They use too much energy.” This is particularly relevant given the level of energy needed to run large software models with a huge amount of data like machine learning for artificial intelligence.

Yang goes on to explain that, unlike the brain, “Our existing computing systems were never intended to process massive amounts of data or to learn from just a few examples on their own. One way to boost both energy and learning efficiency is to build artificial systems that operate according to principles observed in the brain.”

If you are looking for pure speed, electrons that run modern computing would be the best for fast operations. But, he explains, “Ions are a better medium than electrons for embodying principles of the brain. Because electrons are lightweight and volatile, computing with them enables software-based learning rather than hardware-based learning, which is fundamentally different from how the brain operates.”

In contrast, he says, “The brain learns by moving ions across membranes, achieving energy-efficient and adaptive learning directly in hardware, or more precisely, in what people may call ‘wetware’.”

For example, a young child can learn to recognize handwritten digits after seeing only a few examples of each, whereas a computer typically needs thousands to achieve the same task. Yet, the human brain accomplishes this remarkable learning while consuming only about 20 watts of power, compared to the megawatts required by today’s supercomputers.

Potential Impact

This new method is one step closer to mimicking natural intelligence.

Yang noted that silver used in the experiment is not readily compatible with conventional semiconductor manufacturing, and that alternative ionic species will need to be investigated for similar functionalities.

The efficiency of these diffusive memristors include not only the energy, but size. Normally, one smart phone has about 10 chips but billions of transistors or switches that control the on/off or 0’s and 1’s that underpin computation.

“Instead [with this innovation], we just use a footprint of one transistor for each neuron. We are designing the building blocks that eventually led us to reduce the chip size by orders of magnitude, reduce the energy consumption by orders of magnitude, so it can be sustainable to perform AI in the future, with similar level of intelligence without burning energy that we cannot sustain,” says Yang.

Now that we have demonstrated capable and compact building blocks, artificial synapses and neurons, the next step is to integrate large numbers of them and test how closely we can replicate the brain’s efficiency and capabilities. “Even more exciting,” says Yang, “is the prospect that such brain-faithful systems could help us uncover new insights into how the brain itself works.”

Reference: “A spiking artificial neuron based on one diffusive memristor, one transistor and one resistor” by Ruoyu Zhao, Tong Wang, Taehwan Moon, Yichun Xu, Jian Zhao, Piyush Sud, Seung Ju Kim, Han-Ting Liao, Ye Zhuo, Rivu Midya, Shiva Asapu, Dawei Gao, Zixuan Rong, Qinru Qiu, Cynthia Bowers, Krishnamurthy Mahalingam, S. Ganguli, A. K. Roy, Qing Wu, Jin-Woo Han, R. Stanley Williams, Yong Chen and J. Joshua Yang, 27 October 2025, Nature Electronics.
DOI: 10.1038/s41928-025-01488-x

Funding: Army Research Office, Army Research Office, MURI, Center of Excellence, Air Force Research Laboratory, U.S. National Science Foundation, U.S. National Science Foundation

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