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Artificial neurons replicate real brain processes with ion-based diffusive memristors

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A November 5, 2025 news item on ScienceDaily announced memristors that more closely resemble biological neurons,

USC [University of Southern California] researchers built artificial neurons that replicate real brain processes using ion-based diffusive memristors. These devices 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. It could transform AI into something closer to natural intelligence.

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An October 28, 2026 University of Southern California (USC) news release (also on Eurekalert but published October 29, 2025, which originated the news item, provides more detail about this engineering feat,

Researchers at the USC Viterbi School of Engineering and School of Advanced Computing have developed artificial neurons that replicate the complex electrochemical behavior of biological brain cells. The innovation, documented in Nature Electronics, is a leap forward in neuromorphic computing technology. The innovation will allow for reduction in the chip size by orders of magnitude, reduce its energy consumption by orders of magnitude and could advance artificial general intelligence.

Unlike conventional digital processors or existing neuromorphic chips based on silicon technology that merely simulate neural activity, these artificial neurons physically embody or emulate the analog dynamics of their biological counterparts. Just as neurochemicals initiate brain activity, chemicals can be used to initiate computation in neuromorphic, or-brain-inspired hardware devices. By being a physical replication of the biological process, they differ from prior iterations of artificial neurons that were solely mathematical equations.

The work, led by USC Computer and Electrical Engineering Professor Joshua Yang, who also led the work in a seminal paper on artificial synapses over a decade ago, introduces a new type of artificial neuron based on the so-called “diffusive memristor”. The Nature Electronics paper explores how such artificial neurons can enable a new class of chips that complement and augment today’s silicon-based technologies, which power nearly all modern electronics and rely on the movement of electrons for computation. Instead, the diffusive device introduced by Yang and colleagues to build the neurons would rely on movement of atoms. Such neurons can enable newer chips that would operate more similarly to how our brain works, would be more energy efficient and could lend themselves to usher in what’s known as artificial general intelligence (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.”

Here’s a link to and a citation for the paper,

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 & J. Joshua Yang. Nature Electronics volume 8, pages 1211–1221 (2025) Published: 27 October 2025 Version of record: 27 October 2025 Issue date: December 2025 DOI: https://doi.org/10.1038/s41928-025-01488-x

This paper is behind a paywall.

Combining silicon with metal oxide memristors to create powerful, low-energy intensive chips enabling AI in portable devices

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In this one week, I’m publishing my first stories (see also June 13, 2023 posting “ChatGPT and a neuromorphic [brainlike] synapse“) where artificial intelligence (AI) software is combined with a memristor (hardware component) for brainlike (neuromorphic) computing.

Here’s more about some of the latest research from a March 30, 2023 news item on ScienceDaily,

Everyone is talking about the newest AI and the power of neural networks, forgetting that software is limited by the hardware on which it runs. But it is hardware, says USC [University of Southern California] Professor of Electrical and Computer Engineering Joshua Yang, that has become “the bottleneck.” Now, Yang’s new research with collaborators might change that. They believe that they have developed a new type of chip with the best memory of any chip thus far for edge AI (AI in portable devices).

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A March 29, 2023 University of Southern California (USC) news release (also on EurekAlert), which originated the news item, contextualizes the research and delves further into the topic of neuromorphic hardware,

For approximately the past 30 years, while the size of the neural networks needed for AI and data science applications doubled every 3.5 months, the hardware capability needed to process them doubled only every 3.5 years. According to Yang, hardware presents a more and more severe problem for which few have patience. 

Governments, industry, and academia are trying to address this hardware challenge worldwide. Some continue to work on hardware solutions with silicon chips, while others are experimenting with new types of materials and devices.  Yang’s work falls into the middle—focusing on exploiting and combining the advantages of the new materials and traditional silicon technology that could support heavy AI and data science computation. 

Their new paper in Nature focuses on the understanding of fundamental physics that leads to a drastic increase in memory capacity needed for AI hardware. The team led by Yang, with researchers from USC (including Han Wang’s group), MIT [Massachusetts Institute of Technology], and the University of Massachusetts, developed a protocol for devices to reduce “noise” and demonstrated the practicality of using this protocol in integrated chips. This demonstration was made at TetraMem, a startup company co-founded by Yang and his co-authors  (Miao Hu, Qiangfei Xia, and Glenn Ge), to commercialize AI acceleration technology. According to Yang, this new memory chip has the highest information density per device (11 bits) among all types of known memory technologies thus far. Such small but powerful devices could play a critical role in bringing incredible power to the devices in our pockets. The chips are not just for memory but also for the processor. And millions of them in a small chip, working in parallel to rapidly run your AI tasks, could only require a small battery to power it. 

The chips that Yang and his colleagues are creating combine silicon with metal oxide memristors in order to create powerful but low-energy intensive chips. The technique focuses on using the positions of atoms to represent information rather than the number of electrons (which is the current technique involved in computations on chips). The positions of the atoms offer a compact and stable way to store more information in an analog, instead of digital fashion. Moreover, the information can be processed where it is stored instead of being sent to one of the few dedicated ‘processors,’ eliminating the so-called ‘von Neumann bottleneck’ existing in current computing systems.  In this way, says Yang, computing for AI is “more energy efficient with a higher throughput.”

How it works: 

Yang explains that electrons which are manipulated in traditional chips, are “light.” And this lightness, makes them prone to moving around and being more volatile.  Instead of storing memory through electrons, Yang and collaborators are storing memory in full atoms. Here is why this memory matters. Normally, says Yang, when one turns off a computer, the information memory is gone—but if you need that memory to run a new computation and your computer needs the information all over again, you have lost both time and energy.  This new method, focusing on activating atoms rather than electrons, does not require battery power to maintain stored information. Similar scenarios happen in AI computations, where a stable memory capable of high information density is crucial. Yang imagines this new tech that may enable powerful AI capability in edge devices, such as Google Glasses, which he says previously suffered from a frequent recharging issue.

Further, by converting chips to rely on atoms as opposed to electrons, chips become smaller.  Yang adds that with this new method, there is more computing capacity at a smaller scale. And this method, he says, could offer “many more levels of memory to help increase information density.” 

To put it in context, right now, ChatGPT is running on a cloud. The new innovation, followed by some further development, could put the power of a mini version of ChatGPT in everyone’s personal device. It could make such high-powered tech more affordable and accessible for all sorts of applications. 

Here’s a link to and a citation for the paper,

Thousands of conductance levels in memristors integrated on CMOS by Mingyi Rao, Hao Tang, Jiangbin Wu, Wenhao Song, Max Zhang, Wenbo Yin, Ye Zhuo, Fatemeh Kiani, Benjamin Chen, Xiangqi Jiang, Hefei Liu, Hung-Yu Chen, Rivu Midya, Fan Ye, Hao Jiang, Zhongrui Wang, Mingche Wu, Miao Hu, Han Wang, Qiangfei Xia, Ning Ge, Ju Li & J. Joshua Yang. Nature volume 615, pages 823–829 (2023) DOI: https://doi.org/10.1038/s41586-023-05759-5 Issue Date: 30 March 2023 Published: 29 March 2023

This paper is behind a paywall.

A new memristor circuit

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Apparently engineers at the University of Massachusetts at Amherst have developed a new kind of memristor. A Sept. 29, 2016 news item on Nanowerk makes the announcement (Note: A link has been removed),

Engineers at the University of Massachusetts Amherst are leading a research team that is developing a new type of nanodevice for computer microprocessors that can mimic the functioning of a biological synapse—the place where a signal passes from one nerve cell to another in the body. The work is featured in the advance online publication of Nature Materials (“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing”).

Such neuromorphic computing in which microprocessors are configured more like human brains is one of the most promising transformative computing technologies currently under study.

While it doesn’t sound different from any other memristor, that’s misleading. Do read on. A Sept. 27, 2016 University of Massachusetts at Amherst news release, which originated the news item, provides more detail about the researchers and the work,

J. Joshua Yang and Qiangfei Xia are professors in the electrical and computer engineering department in the UMass Amherst College of Engineering. Yang describes the research as part of collaborative work on a new type of memristive device.

Memristive devices are electrical resistance switches that can alter their resistance based on the history of applied voltage and current. These devices can store and process information and offer several key performance characteristics that exceed conventional integrated circuit technology.

“Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size,” the researchers say.

Neuromorphic computing—meaning microprocessors configured more like human brains than like traditional computer chips—is one of the most promising transformative computing technologies currently under intensive study. Xia says, “This work opens a new avenue of neuromorphic computing hardware based on memristors.”

They say that most previous work in this field with memristors has not implemented diffusive dynamics without using large standard technology found in integrated circuits commonly used in microprocessors, microcontrollers, static random access memory and other digital logic circuits.

The researchers say they proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the standard technology for integrated circuits while sharing great similarities with synapses. They say, “Specifically, we developed a diffusive-type memristor where diffusion of atoms offers a similar dynamics [?] and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses, i.e., a true synaptic emulator.”

The researchers say, “The results here provide an encouraging pathway toward synaptic emulation using diffusive memristors for neuromorphic computing.”

Here’s a link to and a citation for the paper,

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing by Zhongrui Wang, Saumil Joshi, Sergey E. Savel’ev, Hao Jiang, Rivu Midya, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, Qing Wu, Mark Barnell, Geng-Lin Li, Huolin L. Xin, R. Stanley Williams [emphasis mine], Qiangfei Xia, & J. Joshua Yang. Nature Materials (2016) doi:10.1038/nmat4756 Published online 26 September 2016

This paper is behind a paywall.

I’ve emphasized R. Stanley Williams’ name as he was the lead researcher on the HP Labs team that proved Leon Chua’s 1971 theory about the memristor and exerted engineering control of the memristor in 2008. (Bernard Widrow, in the 1960s,  predicted and proved the existence of something he termed a ‘memistor’. Chua arrived at his ‘memristor’ theory independently.)

Austin Silver in a Sept. 29, 2016 posting on The Human OS blog (on the IEEE [Institute of Electrical and Electronics Engineers] website) delves into this latest memristor research (Note: Links have been removed),

In research published in Nature Materials on 26 September [2016], Yang and his team mimicked a crucial underlying component of how synaptic connections get stronger or weaker: the flow of calcium.

The movement of calcium into or out of the neuronal membrane, neuroscientists have found, directly affects the connection. Chemical processes move the calcium in and out— triggering a long-term change in the synapses’ strength. 2015 research in ACS NanoLetters and Advanced Functional Materials discovered that types of memristors can simulate some of the calcium behavior, but not all.

In the new research, Yang combined two types of memristors in series to create an artificial synapse. The hybrid device more closely mimics biological synapse behavior—the calcium flow in particular, Yang says.

The new memristor used–called a diffusive memristor because atoms in the resistive material move even without an applied voltage when the device is in the high resistance state—was a dielectic film sandwiched between Pt [platinum] or Au [gold] electrodes. The film contained Ag [silver] nanoparticles, which would play the role of calcium in the experiments.

By tracking the movement of the silver nanoparticles inside the diffusive memristor, the researchers noticed a striking similarity to how calcium functions in biological systems.

A voltage pulse to the hybrid device drove silver into the gap between the diffusive memristor’s two electrodes–creating a filament bridge. After the pulse died away, the filament started to break and the silver moved back— resistance increased.

Like the case with calcium, a force made silver go in and a force made silver go out.

To complete the artificial synapse, the researchers connected the diffusive memristor in series to another type of memristor that had been studied before.

When presented with a sequence of voltage pulses with particular timing, the artificial synapse showed the kind of long-term strengthening behavior a real synapse would, according to the researchers. “We think it is sort of a real emulation, rather than simulation because they have the physical similarity,” Yang says.

I was glad to find some additional technical detail about this new memristor and to find the Human OS blog, which is new to me and according to its home page is a “biomedical blog, featuring the wearable sensors, big data analytics, and implanted devices that enable new ventures in personalized medicine.”