Category Archives: electronics

Brainlike transistor and human intelligence

This brainlike transistor (not a memristor) is important because it functions at room temperature as opposed to others, which require cryogenic temperatures.

A December 20, 2023 Northwestern University news release (received via email; also on EurekAlert) fills in the details,

  • Researchers develop transistor that simultaneously processes and stores information like the human brain
  • Transistor goes beyond categorization tasks to perform associative learning
  • Transistor identified similar patterns, even when given imperfect input
  • Previous similar devices could only operate at cryogenic temperatures; new transistor operates at room temperature, making it more practical

EVANSTON, Ill. — Taking inspiration from the human brain, researchers have developed a new synaptic transistor capable of higher-level thinking.

Designed by researchers at Northwestern University, Boston College and the Massachusetts Institute of Technology (MIT), the device simultaneously processes and stores information just like the human brain. In new experiments, the researchers demonstrated that the transistor goes beyond simple machine-learning tasks to categorize data and is capable of performing associative learning.

Although previous studies have leveraged similar strategies to develop brain-like computing devices, those transistors cannot function outside cryogenic temperatures. The new device, by contrast, is stable at room temperatures. It also operates at fast speeds, consumes very little energy and retains stored information even when power is removed, making it ideal for real-world applications.

The study was published today (Dec. 20 [2023]) in the journal Nature.

“The brain has a fundamentally different architecture than a digital computer,” said Northwestern’s Mark C. Hersam, who co-led the research. “In a digital computer, data move back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain.”

Hersam is the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern’s McCormick School of Engineering. He also is chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center and member of the International Institute for Nanotechnology. Hersam co-led the research with Qiong Ma of Boston College and Pablo Jarillo-Herrero of MIT.

Recent advances in artificial intelligence (AI) have motivated researchers to develop computers that operate more like the human brain. Conventional, digital computing systems have separate processing and storage units, causing data-intensive tasks to devour large amounts of energy. With smart devices continuously collecting vast quantities of data, researchers are scrambling to uncover new ways to process it all without consuming an increasing amount of power. Currently, the memory resistor, or “memristor,” is the most well-developed technology that can perform combined processing and memory function. But memristors still suffer from energy costly switching.

“For several decades, the paradigm in electronics has been to build everything out of transistors and use the same silicon architecture,” Hersam said. “Significant progress has been made by simply packing more and more transistors into integrated circuits. You cannot deny the success of that strategy, but it comes at the cost of high power consumption, especially in the current era of big data where digital computing is on track to overwhelm the grid. We have to rethink computing hardware, especially for AI and machine-learning tasks.”

To rethink this paradigm, Hersam and his team explored new advances in the physics of moiré patterns, a type of geometrical design that arises when two patterns are layered on top of one another. When two-dimensional materials are stacked, new properties emerge that do not exist in one layer alone. And when those layers are twisted to form a moiré pattern, unprecedented tunability of electronic properties becomes possible.

For the new device, the researchers combined two different types of atomically thin materials: bilayer graphene and hexagonal boron nitride. When stacked and purposefully twisted, the materials formed a moiré pattern. By rotating one layer relative to the other, the researchers could achieve different electronic properties in each graphene layer even though they are separated by only atomic-scale dimensions. With the right choice of twist, researchers harnessed moiré physics for neuromorphic functionality at room temperature.

“With twist as a new design parameter, the number of permutations is vast,” Hersam said. “Graphene and hexagonal boron nitride are very similar structurally but just different enough that you get exceptionally strong moiré effects.”

To test the transistor, Hersam and his team trained it to recognize similar — but not identical — patterns. Just earlier this month, Hersam introduced a new nanoelectronic device capable of analyzing and categorizing data in an energy-efficient manner, but his new synaptic transistor takes machine learning and AI one leap further.

“If AI is meant to mimic human thought, one of the lowest-level tasks would be to classify data, which is simply sorting into bins,” Hersam said. “Our goal is to advance AI technology in the direction of higher-level thinking. Real-world conditions are often more complicated than current AI algorithms can handle, so we tested our new devices under more complicated conditions to verify their advanced capabilities.”

First the researchers showed the device one pattern: 000 (three zeros in a row). Then, they asked the AI to identify similar patterns, such as 111 or 101. “If we trained it to detect 000 and then gave it 111 and 101, it knows 111 is more similar to 000 than 101,” Hersam explained. “000 and 111 are not exactly the same, but both are three digits in a row. Recognizing that similarity is a higher-level form of cognition known as associative learning.”

In experiments, the new synaptic transistor successfully recognized similar patterns, displaying its associative memory. Even when the researchers threw curveballs — like giving it incomplete patterns — it still successfully demonstrated associative learning.

“Current AI can be easy to confuse, which can cause major problems in certain contexts,” Hersam said. “Imagine if you are using a self-driving vehicle, and the weather conditions deteriorate. The vehicle might not be able to interpret the more complicated sensor data as well as a human driver could. But even when we gave our transistor imperfect input, it could still identify the correct response.”

The study, “Moiré synaptic transistor with room-temperature neuromorphic functionality,” was primarily supported by the National Science Foundation.

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

Moiré synaptic transistor with room-temperature neuromorphic functionality by Xiaodong Yan, Zhiren Zheng, Vinod K. Sangwan, Justin H. Qian, Xueqiao Wang, Stephanie E. Liu, Kenji Watanabe, Takashi Taniguchi, Su-Yang Xu, Pablo Jarillo-Herrero, Qiong Ma & Mark C. Hersam. Nature volume 624, pages 551–556 (2023) DOI: https://doi.org/10.1038/s41586-023-06791-1 Published online: 20 December 2023 Issue Date: 21 December 2023

This paper is behind a paywall.

Transformative potential of Martian nanomaterials

Yes, nanomaterials from Mars! A December 21, 2023 news item on Nanowerk makes the proposition, Note: A link has been removed,

Researchers at the University of Sussex have discovered the transformative potential of Martian nanomaterials, potentially opening the door to sustainable habitation on the red planet. They published their findings in (“Quasi–1D Anhydrite Nanobelts from the Sustainable Liquid Exfoliation of Terrestrial Gypsum for Future Martian-Based Electronics”).

Using resources and techniques currently applied on the International Space Station [ISS] and by NASA [US National Aeronautics and Space Administration], Dr Conor Boland, a Lecturer in Materials Physics at the University of Sussex, led a research group that investigated the potential of nanomaterials – incredibly tiny components thousands of times smaller than a human hair – for clean energy production and building materials on Mars.

Taking what was considered a waste product by NASA and applying only sustainable production methods, including water-based chemistry and low-energy processes, the researchers have successfully identified electrical properties within gypsum nanomaterials – opening the door to potential clean energy and sustainable technology production on Mars.

A December 21, 2023 University of Sussex press release (also on EurekAlert) by Stephanie Allen, which originated the news item, features the lead researcher’s hopes for the discovery, Note: A link has been removed,

Dr Conor Boland, said: 

“This study shows that the potential is quite literally out of this world for nanomaterials. Our study builds off recent research performed by NASA and takes what was considered waste, essentially lumps of rock, and turns it into transformative nanomaterials for a range of applications from creating clean hydrogen fuel to developing an electronic device similar to a transistor, to creating an additive to textiles to increase their robustness.

“This opens avenues for sustainable technology – and building – on Mars but also highlights the broader potential for eco-friendly breakthroughs here on Earth.”

To make the breakthrough the researchers used NASA’s innovative method for extracting water from Martian gypsum, which is dehydrated by the agency to get water for human consumption. This produces a byproduct called anhydrite—considered waste material by NASA, but now shown to be hugely valuable.

The Sussex researchers processed anhydrite into nanobelts –  essentially tagliatelle-shaped materials – demonstrating their potential to provide clean energy and sustainable electronics. Furthermore, at every step of their process, water could be continuously collected and recycled.

Dr Boland added: 

“We are optimistic of the feasibility of this process on Mars, as it requires only naturally occurring materials – everything we used could, in theory, be replicated on the red planet. Arguably this is the most important goal in making the Martian colony sustainable from the outset.”

While full-scale electronics production may be impractical on Mars due to the lack of clean rooms and sterile conditions, the anhydrite nanobelts hold promise for clean energy production on Earth, and could, later down the line, still have a profound effect on sustainable energy production on Mars.

Here’s what a Martian nanomaterial looks like,

Caption: Two raw rocks used by the researchers (left). Vials show the nanobelts in water, with a close up of the actual nanobelts (right). Credit: University of Sussex

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

Quasi–1D Anhydrite Nanobelts from the Sustainable Liquid Exfoliation of Terrestrial Gypsum for Future Martian-Based Electronics by Cencen Wei, Abhijit Roy, Adel K. A. Aljarid, Yi Hu, S. Mark Roe, Dimitrios G. Papageorgiou, Raul Arenal, Conor S. Boland. Advanced Functional Materials DOI: https://doi.org/10.1002/adfm.202310600 First published: 14 December 2023

This paper is open access.

Brain-inspired (neuromrophic) computing with twisted magnets and a patent for manufacturing permanent magnets without rare earths

I have two news bits both of them concerned with magnets.

Patent for magnets that can be made without rare earths

I’m starting with the patent news first since this is (as the company notes in its news release) a “Landmark Patent Issued for Technology Critically Needed to Combat Chinese Monopoly.”

For those who don’t know, China supplies most of the rare earths used in computers, smart phones, and other devices. On general principles, having a single supplier dominate production of and access to a necessary material for devices that most of us rely on can raise tensions. Plus, you can’t mine for resources forever.

This December 19, 2023 Nanocrystal Technology LP news release heralds an exciting development (for the impatient, further down the page I have highlighted the salient sections),

Nanotechnology Discovery by 2023 Nobel Prize Winner Became Launch Pad to Create Permanent Magnets without Rare Earths from China

NEW YORK, NY, UNITED STATES, December 19, 2023 /EINPresswire.com/ — Integrated Nano-Magnetics Corp, a wholly owned subsidiary of Nanocrystal Technology LP, was awarded a patent for technology built upon a fundamental nanoscience discovery made by Aleksey Yekimov, its former Chief Scientific Officer.

This patent will enable the creation of strong permanent magnets which are critically needed for both industrial and military applications but cannot be manufactured without certain “rare earth” elements available mostly from China.

At a glittering awards ceremony held in Stockholm on December10, 2023, three scientists, Aleksey Yekimov, Louis Brus (Professor at Columbia University) and Moungi Bawendi (Professor at MIT) were honored with the Nobel Prize in Chemistry for their discovery of the “quantum dot” which is now fueling practical applications in tuning the colors of LEDs, increasing the resolution of TV screens, and improving MRI imaging.

As stated by the Royal Swedish Academy of Sciences, “Quantum dots are … bringing the greatest benefits to humankind. Researchers believe that in the future they could contribute to flexible electronics, tiny sensors, thinner solar cells, and encrypted quantum communications – so we have just started exploring the potential of these tiny particles.”

Aleksey Yekimov worked for over 19 years until his retirement as Chief Scientific Officer of Nanocrystals Technology LP, an R & D company in New York founded by two Indian-American entrepreneurs, Rameshwar Bhargava and Rajan Pillai.

Yekimov, who was born in Russia, had already received the highest scientific honors for his work before he immigrated to USA in 1999. Yekimov was greatly intrigued by Nanocrystal Technology’s research project and chose to join the company as its Chief Scientific Officer.

During its early years, the company worked on efficient light generation by doping host nanoparticles about the same size as a quantum dot with an additional impurity atom. Bhargava came up with the novel idea of incorporating a single impurity atom, a dopant, into a quantum dot sized host, and thus achieve an extraordinary change in the host material’s properties such as inducing strong permanent magnetism in weak, readily available paramagnetic materials. To get a sense of the scale at which nanotechnology works, and as vividly illustrated by the Nobel Foundation, the difference in size between a quantum dot and a soccer ball is about the same as the difference between a soccer ball and planet Earth.

Currently, strong permanent magnets are manufactured from “rare earths” available mostly in China which has established a near monopoly on the supply of rare-earth based strong permanent magnets. Permanent magnets are a fundamental building block for electro-mechanical devices such as motors found in all automobiles including electric vehicles, trucks and tractors, military tanks, wind turbines, aircraft engines, missiles, etc. They are also required for the efficient functioning of audio equipment such as speakers and cell phones as well as certain magnetic storage media.

The existing market for permanent magnets is $28 billion and is projected to reach $50 billion by 2030 in view of the huge increase in usage of electric vehicles. China’s overwhelming dominance in this field has become a matter of great concern to governments of all Western and other industrialized nations. As the Wall St. Journal put it, China’s now has a “stranglehold” on the economies and security of other countries.

The possibility of making permanent magnets without the use of any rare earths mined in China has intrigued leading physicists and chemists for nearly 30 years. On December 19, 2023, a U.S. patent with the title ‘’Strong Non Rare Earth Permanent Magnets from Double Doped Magnetic Nanoparticles” was granted to Integrated Nano-Magnetics Corp. [emphasis mine] Referring to this major accomplishment Bhargava said, “The pioneering work done by Yekimov, Brus and Bawendi has provided the foundation for us to make other discoveries in nanotechnology which will be of great benefit to the world.”

I was not able to find any company websites. The best I could find is a Nanocrystals Technology LinkedIn webpage and some limited corporate data for Integrated Nano-Magnetics on opencorporates.com.

Twisted magnets and brain-inspired computing

This research offers a pathway to neuromorphic (brainlike) computing with chiral (or twisted) magnets, which, as best as I understand it, do not require rare earths. From a November13, 2023 news item on ScienceDaily,

A form of brain-inspired computing that exploits the intrinsic physical properties of a material to dramatically reduce energy use is now a step closer to reality, thanks to a new study led by UCL [University College London] and Imperial College London [ICL] researchers.

In the new study, published in the journal Nature Materials, an international team of researchers used chiral (twisted) magnets as their computational medium and found that, by applying an external magnetic field and changing temperature, the physical properties of these materials could be adapted to suit different machine-learning tasks.

A November 9, 2023 UCL press release (also on EurekAlert but published November 13, 2023), which originated the news item, fill s in a few more details about the research,

Dr Oscar Lee (London Centre for Nanotechnology at UCL and UCL Department of Electronic & Electrical Engineering), the lead author of the paper, said: “This work brings us a step closer to realising the full potential of physical reservoirs to create computers that not only require significantly less energy, but also adapt their computational properties to perform optimally across various tasks, just like our brains.

“The next step is to identify materials and device architectures that are commercially viable and scalable.”

Traditional computing consumes large amounts of electricity. This is partly because it has separate units for data storage and processing, meaning information has to be shuffled constantly between the two, wasting energy and producing heat. This is particularly a problem for machine learning, which requires vast datasets for processing. Training one large AI model can generate hundreds of tonnes of carbon dioxide.

Physical reservoir computing is one of several neuromorphic (or brain inspired) approaches that aims to remove the need for distinct memory and processing units, facilitating more efficient ways to process data. In addition to being a more sustainable alternative to conventional computing, physical reservoir computing could be integrated into existing circuitry to provide additional capabilities that are also energy efficient.

In the study, involving researchers in Japan and Germany, the team used a vector network analyser to determine the energy absorption of chiral magnets at different magnetic field strengths and temperatures ranging from -269 °C to room temperature.

They found that different magnetic phases of chiral magnets excelled at different types of computing task. The skyrmion phase, where magnetised particles are swirling in a vortex-like pattern, had a potent memory capacity apt for forecasting tasks. The conical phase, meanwhile, had little memory, but its non-linearity was ideal for transformation tasks and classification – for instance, identifying if an animal is a cat or dog.

Co-author Dr Jack Gartside, of Imperial College London, said: “Our collaborators at UCL in the group of Professor Hidekazu Kurebayashi recently identified a promising set of materials for powering unconventional computing. These materials are special as they can support an especially rich and varied range of magnetic textures. Working with the lead author Dr Oscar Lee, the Imperial College London group [led by Dr Gartside, Kilian Stenning and Professor Will Branford] designed a neuromorphic computing architecture to leverage the complex material properties to match the demands of a diverse set of challenging tasks. This gave great results, and showed how reconfiguring physical phases can directly tailor neuromorphic computing performance.”

The work also involved researchers at the University of Tokyo and Technische Universität München and was supported by the Leverhulme Trust, Engineering and Physical Sciences Research Council (EPSRC), Imperial College London President’s Excellence Fund for Frontier Research, Royal Academy of Engineering, the Japan Science and Technology Agency, Katsu Research Encouragement Award, Asahi Glass Foundation, and the DFG (German Research Foundation).

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

Task-adaptive physical reservoir computing by Oscar Lee, Tianyi Wei, Kilian D. Stenning, Jack C. Gartside, Dan Prestwood, Shinichiro Seki, Aisha Aqeel, Kosuke Karube, Naoya Kanazawa, Yasujiro Taguchi, Christian Back, Yoshinori Tokura, Will R. Branford & Hidekazu Kurebayashi. Nature Materials volume 23, pages 79–87 (2024) DOI: https://doi.org/10.1038/s41563-023-01698-8 Published online: 13 November 2023 Issue Date: January 2024

This paper is open access.

Everlasting dirt-powered sensors for agriculture?

Caption: The fuel cell’s 3D printed cap peeks above the ground. The cap keeps debris out of the device while enabling air flow. Credit: Bill Yen/Northwestern University

A January 12, 2024 Northwestern University news release (also received via email and also on EurekAlert both published January 15, 2024) describes this dirt-powered research from the US, Note: Links have been removed,

*New fuel cell harnesses naturally occurring microbes to generate electricity

*Soil-powered sensors to successfully monitor soil moisture and detect touch

*New tech was robust enough to withstand drier soil conditions and flooding

*Fuel cell could replace batteries in sensors used for precision agriculture

EVANSTON, Ill. — A Northwestern University-led team of researchers has developed a new fuel cell that harvests energy from microbes living in dirt. 

About the size of a standard paperback book, the completely soil-powered technology could fuel underground sensors used in precision agriculture and green infrastructure. This potentially could offer a sustainable, renewable alternative to batteries, which hold toxic, flammable chemicals that leach into the ground, are fraught with conflict-filled supply chains and contribute to the ever-growing problem of electronic waste.

To test the new fuel cell, the researchers used it to power sensors measuring soil moisture and detecting touch, a capability that could be valuable for tracking passing animals. To enable wireless communications, the researchers also equipped the soil-powered sensor with a tiny antenna to transmit data to a neighboring base station by reflecting existing radio frequency signals.

Not only did the fuel cell work in both wet and dry conditions, but its power also outlasted similar technologies by 120%.

The research will be published today (Jan. 12 [2024]) in the Proceedings of the Association for Computing Machinery on Interactive, Mobile, Wearable and Ubiquitous Technologies. The study authors also are releasing all designs, tutorials and simulation tools to the public, so others may use and build upon the research.

“The number of devices in the Internet of Things (IoT) is constantly growing,” said Northwestern alumnus Bill Yen, who led the work. “If we imagine a future with trillions of these devices, we cannot build every one of them out of lithium, heavy metals and toxins that are dangerous to the environment. We need to find alternatives that can provide low amounts of energy to power a decentralized network of devices. In a search for solutions, we looked to soil microbial fuel cells, which use special microbes to break down soil and use that low amount of energy to power sensors. As long as there is organic carbon in the soil for the microbes to break down, the fuel cell can potentially last forever.”

“These microbes are ubiquitous; they already live in soil everywhere,” said Northwestern’s George Wells, a senior author on the study. “We can use very simple engineered systems to capture their electricity. We’re not going to power entire cities with this energy. But we can capture minute amounts of energy to fuel practical, low-power applications.”

Wells is an associate professor of civil and environmental engineering at Northwestern’s McCormick School of Engineering. Now a Ph.D. student at Stanford University, Yen started this project when he was an undergraduate researcher in Wells’ laboratory.

Solutions for a dirty job

In recent years, farmers worldwide increasingly have adopted precision agriculture as a strategy to improve crop yields. The tech-driven approach relies on measuring precise levels of moisture, nutrients and contaminants in soil to make decisions that enhance crop health. This requires a widespread, dispersed network of electronic devices to continuously collect environmental data.

“If you want to put a sensor out in the wild, in a farm or in a wetland, you are constrained to putting a battery in it or harvesting solar energy,” Yen said. “Solar panels don’t work well in dirty environments because they get covered with dirt, do not work when the sun isn’t out and take up a lot of space. Batteries also are challenging because they run out of power. Farmers are not going to go around a 100-acre farm to regularly swap out batteries or dust off solar panels.”

To overcome these challenges, Wells, Yen and their collaborators wondered if they could instead harvest energy from the existing environment. “We could harvest energy from the soil that farmers are monitoring anyway,” Yen said.

‘Stymied efforts’

Making their first appearance in 1911, soil-based microbial fuel cells (MFCs) operate like a battery — with an anode, cathode and electrolyte. But instead of using chemicals to generate electricity, MFCs harvest electricity from bacteria that naturally donate electrons to nearby conductors. When these electrons flow from the anode to the cathode, it creates an electric circuit.

But in order for microbial fuel cells to operate without disruption, they need to stay hydrated and oxygenated — which is tricky when buried underground within dry dirt.

“Although MFCs have existed as a concept for more than a century, their unreliable performance and low output power have stymied efforts to make practical use of them, especially in low-moisture conditions,” Yen said.

Winning geometry

With these challenges in mind, Yen and his team embarked on a two-year journey to develop a practical, reliable soil-based MFC. His expedition included creating — and comparing — four different versions. First, the researchers collected a combined nine months of data on the performance of each design. Then, they tested their final version in an outdoor garden.

The best-performing prototype worked well in dry conditions as well as within a water-logged environment. The secret behind its success: Its geometry. Instead of using a traditional design, in which the anode and cathode are parallel to one another, the winning fuel cell leveraged a perpendicular design.

Made of carbon felt (an inexpensive, abundant conductor to capture the microbes’ electrons), the anode is horizontal to the ground’s surface. Made of an inert, conductive metal, the cathode sits vertically atop the anode. 

Although the entire device is buried, the vertical design ensures that the top end is flush with the ground’s surface. A 3D-printed cap rests on top of the device to prevent debris from falling inside. And a hole on top and an empty air chamber running alongside the cathode enable consistent airflow.  

The lower end of the cathode remains nestled deep beneath the surface, ensuring that it stays hydrated from the moist, surrounding soil — even when the surface soil dries out in the sunlight. The researchers also coated part of the cathode with waterproofing material to allow it to breathe during a flood. And, after a potential flood, the vertical design enables the cathode to dry out gradually rather than all at once.

On average, the resulting fuel cell generated 68 times more power than needed to operate its sensors. It also was robust enough to withstand large changes in soil moisture — from somewhat dry (41% water by volume) to completely underwater.

Making computing accessible

The researchers say all components for their soil-based MFC can be purchased at a local hardware store. Next, they plan to develop a soil-based MFC made from fully biodegradable materials. Both designs bypass complicated supply chains and avoid using conflict minerals.

“With the COVID-19 pandemic, we all became familiar with how a crisis can disrupt the global supply chain for electronics,” said study co-author Josiah Hester, a former Northwestern faculty member who is now at the Georgia Institute of Technology. “We want to build devices that use local supply chains and low-cost materials so that computing is accessible for all communities.”

The study, “Soil-powered computing: The engineer’s guide to practical soil microbial fuel cell design,” was supported by the National Science Foundation (award number CNS-2038853), the Agricultural and Food Research Initiative (award number 2023-67021-40628) from the USDA National Institute of Food and Agriculture, the Alfred P. Sloan Foundation, VMware Research and 3M.

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

Soil-Powered Computing: The Engineer’s Guide to Practical Soil Microbial Fuel Cell Design by Bill Yen, Laura Jaliff, Louis Gutierrez, Philothei Sahinidis, Sadie Bernstein, John Madden, Stephen Taylor, Colleen Josephson, Pat Pannuto, Weitao Shuai, George Wells, Nivedita Arora, Josiah Hester. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 7 Issue 4 Article No.: 196 pp 1–40 DOI: https://doi.org/10.1145/3631410 Published: 12 January 2024

This paper is open access.

Physical neural network based on nanowires can learn and remember ‘on the fly’

A November 1, 2023 news item on Nanowerk announced new work on neuromorphic engineering from Australia,

For the first time, a physical neural network has successfully been shown to learn and remember ‘on the fly’, in a way inspired by and similar to how the brain’s neurons work.

The result opens a pathway for developing efficient and low-energy machine intelligence for more complex, real-world learning and memory tasks.

Key Takeaways
*The nanowire-based system can learn and remember ‘on the fly,’ processing dynamic, streaming data for complex learning and memory tasks.

*This advancement overcomes the challenge of heavy memory and energy usage commonly associated with conventional machine learning models.

*The technology achieved a 93.4% accuracy rate in image recognition tasks, using real-time data from the MNIST database of handwritten digits.

*The findings promise a new direction for creating efficient, low-energy machine intelligence applications, such as real-time sensor data processing.

Nanowire neural network
Caption: Electron microscope image of the nanowire neural network that arranges itself like ‘Pick Up Sticks’. The junctions where the nanowires overlap act in a way similar to how our brain’s synapses operate, responding to electric current. Credit: The University of Sydney

A November 1, 2023 University of Sydney news release (also on EurekAlert), which originated the news item, elaborates on the research,

Published today [November 1, 2023] in Nature Communications, the research is a collaboration between scientists at the University of Sydney and University of California at Los Angeles.

Lead author Ruomin Zhu, a PhD student from the University of Sydney Nano Institute and School of Physics, said: “The findings demonstrate how brain-inspired learning and memory functions using nanowire networks can be harnessed to process dynamic, streaming data.”

Nanowire networks are made up of tiny wires that are just billionths of a metre in diameter. The wires arrange themselves into patterns reminiscent of the children’s game ‘Pick Up Sticks’, mimicking neural networks, like those in our brains. These networks can be used to perform specific information processing tasks.

Memory and learning tasks are achieved using simple algorithms that respond to changes in electronic resistance at junctions where the nanowires overlap. Known as ‘resistive memory switching’, this function is created when electrical inputs encounter changes in conductivity, similar to what happens with synapses in our brain.

In this study, researchers used the network to recognise and remember sequences of electrical pulses corresponding to images, inspired by the way the human brain processes information.

Supervising researcher Professor Zdenka Kuncic said the memory task was similar to remembering a phone number. The network was also used to perform a benchmark image recognition task, accessing images in the MNIST database of handwritten digits, a collection of 70,000 small greyscale images used in machine learning.

“Our previous research established the ability of nanowire networks to remember simple tasks. This work has extended these findings by showing tasks can be performed using dynamic data accessed online,” she said.

“This is a significant step forward as achieving an online learning capability is challenging when dealing with large amounts of data that can be continuously changing. A standard approach would be to store data in memory and then train a machine learning model using that stored information. But this would chew up too much energy for widespread application.

“Our novel approach allows the nanowire neural network to learn and remember ‘on the fly’, sample by sample, extracting data online, thus avoiding heavy memory and energy usage.”

Mr Zhu said there were other advantages when processing information online.

“If the data is being streamed continuously, such as it would be from a sensor for instance, machine learning that relied on artificial neural networks would need to have the ability to adapt in real-time, which they are currently not optimised for,” he said.

In this study, the nanowire neural network displayed a benchmark machine learning capability, scoring 93.4 percent in correctly identifying test images. The memory task involved recalling sequences of up to eight digits. For both tasks, data was streamed into the network to demonstrate its capacity for online learning and to show how memory enhances that learning.

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

Online dynamical learning and sequence memory with neuromorphic nanowire networks by Ruomin Zhu, Sam Lilak, Alon Loeffler, Joseph Lizier, Adam Stieg, James Gimzewski & Zdenka Kuncic. Nature Communications volume 14, Article number: 6697 (2023) DOI: https://doi.org/10.1038/s41467-023-42470-5 Published: 01 November 2023

This paper is open access.

You’ll notice a number of this team’s members are also listed in the citation in my June 21, 2023 posting “Learning and remembering like a human brain: nanowire networks” and you’ll see some familiar names in the citation in my June 17, 2020 posting “A tangle of silver nanowires for brain-like action.”

“Injectable tissue prosthesis coupled with closed-loop bioelectronic system” for damaged muscle/nerve regeneration and robot-assisted rehabilitation

A fascinating new use for hyaluronic acid (usually discussed in relation to cosmetic wrinkle-reduction) has been found according to a November 1, 2023 news item on ScienceDaily.

In a recent publication in the journal Nature, researchers from the Institute of Basic Science (IBS) in South Korea have made significant strides in biomaterial technology and rehabilitation medicine. They’ve developed a novel approach to healing muscle injury by employing “injectable tissue prosthesis” in the form of conductive hydrogels and combining it with a robot-assisted rehabilitation system.

Let’s imagine you are swimming in the ocean. A giant shark approaches and bites a huge chunk of meat out of your thigh, resulting in a complete loss of motor/sensor function in your leg.

If left untreated, such severe muscle damage would result in permanent loss of function and disability.

How on Earth will you be able to recover from this kind of injury?

Traditional rehabilitation methods for these kinds of muscle injuries have long sought an efficient closed-loop gait rehabilitation system that merges lightweight exoskeletons and wearable/implantable devices.

Such assistive prosthetic system is required to aid the patients through the process of recovering sensory and motor functions linked to nerve and muscle damage.

Unfortunately, the mechanical properties and rigid nature of existing electronic materials render them incompatible with soft tissues.

This leads to friction and potential inflammation, stalling patient rehabilitation.

To overcome these limitations, the IBS researchers turned to a material commonly used as a wrinkle-smoothing filler, called hyaluronic acid.

A November 2, 2023 Institute of Basic Science (IBS) press release (also on EurekAlert but published November 1, 2023), which originated the news item, explains how hyaluronic acid helps in tissue rehabilitation and regeneration,

Using this substance [hyaluronic acid], an injectable hydrogel was developed for “tissue prostheses”, which can temporarily fill the gap of the missing muscle/nerve tissues while it regenerates. The injectable nature of this material gives it a significant advantage over traditional bioelectronic devices, which are unsuitable for narrow, deep, or small areas, and necessitate invasive surgeries.

Thanks to its highly “tissue-like” properties, this hydrogel seamlessly interfaces with biological tissues and can be easily administered to hard-to-reach body areas without surgery. The reversible and irreversible crosslinks within the hydrogel adapt to high shear stress during injection, ensuring excellent mechanical stability. This hydrogel also incorporates gold nanoparticles, which gives it decent electrical properties. Its conductive nature allows for the effective transmission of electrophysiological signals between the two ends of injured tissues. In addition, the hydrogel is biodegrdable, meaning that the patients do not need to get surgery again.

With mechanical properties akin to natural tissues, exceptional tissue adhesion, and injectable characteristics, researchers believe this material offers a novel approach to rehabilitation.

Next, the researchers put this novel idea to the test in rodent models. To simulate volumetric muscle loss injury, a large chunk of muscle has been removed from the hind legs of these animals. By injecting the hydrogel and implanting the two kinds of stretchable tissue-interfacing devices for electrical sensing and stimulation, the researchers were able to improve the gait in the “injured” rodents. The hydrogel prosthetics were combined with robot assistance, guided by muscle electromyography signals. Together, the two helped enhance the animal’s gait without nerve stimulation. Furthermore, muscle tissue regeneration was effectively improved over the long term after the conductive hydrogel was used to fill muscle damage.

The injectable conductive hydrogel developed in this study excels in electrophysiological signal recording and stimulation performance, offering the potential to expand its applications. It presents a fresh approach to the field of bioelectronic devices and holds promise as a soft tissue prosthesis for rehabilitation support.

Emphasizing the significance of the research, Professor SHIN Mikyung notes, “We’ve created an injectable, mechanically tough, and electrically conductive soft tissue prosthesis ideal for addressing severe muscle damage requiring neuromusculoskeletal rehabilitation. The development of this injectable hydrogel, utilizing a novel cross-linking method, is a notable achievement. We believe it will be applicable not only in muscles and peripheral nerves but also in various organs like the brain and heart.”

Professor SON Donghee added, “In this study, the closed-loop gait rehabilitation system entailing tough injectable hydrogel and stretchable and self-healing sensors could significantly enhance the rehabilitation prospects for patients with neurological and musculoskeletal challenges. It could also play a vital role in precise diagnosis and treatment across various organs in the human body.”

The research team is currently pursuing further studies to develop new materials for nerve and muscle tissue regeneration that can be implanted in a minimally invasive manner. They are also exploring the potential for recovery in various tissue damages through the injection of the conductive hydrogel, eliminating the need for open surgery.

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

Injectable tissue prosthesis for instantaneous closed-loop rehabilitation by Subin Jin, Heewon Choi, Duhwan Seong, Chang-Lim You, Jong-Sun Kang, Seunghyok Rho, Won Bo Lee, Donghee Son & Mikyung Shin. Nature volume 623, pages 58–65 (2023) DOI: https://doi.org/10.1038/s41586-023-06628-x Published: 01 November 2023 Issue Date: 02 November 2023

This paper is behind a paywall.

Pressure-cooking birch leaves to produce nanoscale carbon particles for organic semiconductors

By pressure cooking birch leaves picked on campus the scientists produced carbon particles that can be used as raw material for the production of organic semiconductors. Image: Mattias Pettersson

A November 28, 2023 news item on phys.org announces work on an organic semiconductor,

Today, petrochemical compounds and rare metals such as platinum and iridium are used to produce semiconductors for optoelectronics, such as organic LEDs for super-thin TV and mobile phone screens. Physicists at Umeå University in collaboration with researchers in Denmark and China, have discovered a more sustainable alternative. By pressure-cooking birch leaves picked on the Umeå University campus, they have produced a nanosized carbon particle with desired optical properties.

A January 28, 2023 Umeå University press release by Anna-Lena Lindskog, which originated the news item, provides more information about the research,

“The essence of our research is to harness nearby renewable resources for producing organic semiconductor materials” says Jia Wang, research fellow at the Department of Physics, Umeå University, and one of the authors of the study that has been published in the Green Chemistry.

Organic semiconductors are one of the most important functional materials in optoelectronic applications. One example is the organic light-emitting diodes, OLEDs, which enable ultra-thin and bright TV and mobile phone screens. Sharply increasing demand for this advanced technology is driving massive production of organic semiconductor materials.

Unfortunately, these semiconductors are currently produced mainly from petrochemical compounds and rare elements, obtained through environmentally harmful mining. Moreover, these materials often contain so-called ‘critical raw materials’ that are in short supply, such as Platinum, Indium and Phosphorus.

From a sustainability point of view, it would be ideal if we can use biomass from plants, animals and waste to produce organic semiconductor materials. These starting materials are renewable and abundantly available. Research fellow Jia Wang and her colleagues at the Department of Physics, together with international partners, have succeeded in producing such a bio-based semiconductor material.

Birch leaves in pressure cooker

The synthesis process is simple: they picked birch leaves on the Umeå campus and cooked them in a pressure cooker. That produced a kind of ‘carbon dots’ about two nanometers in size, which emit a narrow-band, deep red light when dissolved in ethanol. Some of the optical properties of these birch leaf carbon dots are comparable to commercial quantum dots currently used in semiconductor materials, but unlike them, they contain no heavy metals or critical raw materials.

”It is important to note that our method is not limited to birch leaves” explains Jia Wang. “We tested different plant leaves with the same pressure cooking method, and all of them produced similar red-emitting carbon dots. This versatility suggests that the transformation process can be used in different locations.”

Using the carbon dots in a light-emitting electrochemical cell device, the researchers were able to show that the brightness generated was 100 cd/m2, which is comparable to the light intensity from a computer screen.

“It is important to note that our method is not limited to birch leaves.”

”This result shows that it is possible to transition from depleting petroleum compounds to regenerating biomass as a raw material for organic semiconductors” says Jia Wang.

She emphasises the broader potential of carbon dots beyond just light-emitting devices.

“Carbon dots are promising across various applications, from bioimaging and sensing to anti-counterfeiting. We’re open to collaborations and eager to explore more exciting uses for these emissive and sustainable carbon dots” says Jia Wang.

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

Fluorescent carbon dots from birch leaves for sustainable electroluminescent devices by Shi Tang, Yongfeng Liu, Henry Opoku, Märta Gregorsson, Peijuan Zhang, Etienne Auroux, Dongfeng Dang, Anja-Verena Mudring, Thomas Wågberg, Ludvig Edman, and Jia Wang. Green Chem., 2023, 25, 9884-9895 DOI: https://doi.org/10.1039/D3GC03827K First published: 01 Nov 2023

This paper is open access.

Adaptive neural connectivity with an event-based architecture using photonic processors

On first glance it looked like a set of matches. If there were more dimension, this could also have been a set pencils but no,

Caption: The chip contains almost 8,400 functioning artificial neurons from waveguide-coupled phase-change material. The researchers trained this neural network to distinguish between German and English texts on the basis of vowel frequency. Credit: Jonas Schütte / Pernice Group Courtesy: University of Münster

An October 23, 2023 news item on Nanowerk introduces research into a new approach to optical neural networks

A team of researchers headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster, has developed a so-called event-based architecture, using photonic processors. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network.

Key Takeaways

Researchers have created a new computing architecture that mimics biological neural networks, using photonic processors for data transportation and processing.

The new system enables continuous adaptation of connections within the neural network, crucial for learning processes. This is known as both synaptic and structural plasticity.

Unlike traditional studies, the connections or synapses in this photonic neural network are not hardware-based but are coded based on optical pulse properties, allowing for a single chip to hold several thousand neurons.

Light-based processors in this system offer a much higher bandwidth and lower energy consumption compared to traditional electronic processors.

The researchers successfully tested the system using an evolutionary algorithm to differentiate between German and English texts based on vowel count, highlighting its potential for rapid and energy-efficient AI applications.

The Research

Modern computer models – for example for complex, potent AI applications – push traditional digital computer processes to their limits.

The person who edited the original press release, which is included in the news item in the above, is not credited.

Here’s the unedited original October 23, 2023 University of Münster press release (also on EurekAlert)

Modern computer models – for example for complex, potent AI applications – push traditional digital computer processes to their limits. New types of computing architecture, which emulate the working principles of biological neural networks, hold the promise of faster, more energy-efficient data processing. A team of researchers has now developed a so-called event-based architecture, using photonic processors with which data are transported and processed by means of light. In a similar way to the brain, this makes possible the continuous adaptation of the connections within the neural network. This changeable connections are the basis for learning processes. For the purposes of the study, a team working at Collaborative Research Centre 1459 (“Intelligent Matter”) – headed by physicists Prof. Wolfram Pernice and Prof. Martin Salinga and computer specialist Prof. Benjamin Risse, all from the University of Münster – joined forces with researchers from the Universities of Exeter and Oxford in the UK. The study has been published in the journal “Science Advances”.

What is needed for a neural network in machine learning are artificial neurons which are activated by external excitatory signals, and which have connections to other neurons. The connections between these artificial neurons are called synapses – just like the biological original. For their study, the team of researchers in Münster used a network consisting of almost 8,400 optical neurons made of waveguide-coupled phase-change material, and the team showed that the connection between two each of these neurons can indeed become stronger or weaker (synaptic plasticity), and that new connections can be formed, or existing ones eliminated (structural plasticity). In contrast to other similar studies, the synapses were not hardware elements but were coded as a result of the properties of the optical pulses – in other words, as a result of the respective wavelength and of the intensity of the optical pulse. This made it possible to integrate several thousand neurons on one single chip and connect them optically.

In comparison with traditional electronic processors, light-based processors offer a significantly higher bandwidth, making it possible to carry out complex computing tasks, and with lower energy consumption. This new approach consists of basic research. “Our aim is to develop an optical computing architecture which in the long term will make it possible to compute AI applications in a rapid and energy-efficient way,” says Frank Brückerhoff-Plückelmann, one of the lead authors.

Methodology: The non-volatile phase-change material can be switched between an amorphous structure and a crystalline structure with a highly ordered atomic lattice. This feature allows permanent data storage even without an energy supply. The researchers tested the performance of the neural network by using an evolutionary algorithm to train it to distinguish between German and English texts. The recognition parameter they used was the number of vowels in the text.

The researchers received financial support from the German Research Association, the European Commission and “UK Research and Innovation”.

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

Event-driven adaptive optical neural network by Frank Brückerhoff-Plückelmann, Ivonne Bente, Marlon Becker, Niklas Vollmar, Nikolaos Farmakidis, Emma Lomonte, Francesco Lenzini, C. David Wright, Harish Bhaskaran, Martin Salinga, Benjamin Risse, and Wolfram H. P. Pernice. Science Advances 20 Oct 2023 Vol 9, Issue 42 DOI: 10.1126/sciadv.adi9127

This paper is open access.

Living technology possibilities

Before launching into the possibilities, here are two descriptions of ‘living technology’ from the European Centre for Living Technology’s (ECLT) homepage,

Goals

Promote, carry out and coordinate research activities and the diffusion of scientific results in the field of living technology. The scientific areas for living technology are the nano-bio-technologies, self-organizing and evolving information and production technologies, and adaptive complex systems.

History

Founded in 2004 the European Centre for Living Technology is an international and interdisciplinary research centre established as an inter-university consortium, currently involving 18 European and extra-European institutional affiliates.

The Centre is devoted to the study of technologies that exhibit life-like properties including self-organization, adaptability and the capacity to evolve.

Despite the reference to “nano-bio-technologies,” this October 11, 2023 news item on ScienceDaily focuses on microscale living technology,

It is noIn a recent article in the high-profile journal “Advanced Materials,” researchers in Chemnitz show just how close and necessary the transition to sustainable living technology is, based on the morphogenesis of self-assembling microelectronic modules, strengthening the recent membership of Chemnitz University of Technology with the European Centre for Living Technology (ECLT) in Venice.

An October 11, 2023 Chemnitz University of Technology (Technische Universität Chemnitz; TU Chemnitz) press release (also on EurekAlert), which originated the news item, delves further into the topic, Note: Links have been removed,

It is now apparent that the mass-produced artefacts of technology in our increasingly densely populated world – whether electronic devices, cars, batteries, phones, household appliances, or industrial robots – are increasingly at odds with the sustainable bounded ecosystems achieved by living organisms based on cells over millions of years. Cells provide organisms with soft and sustainable environmental interactions with complete recycling of material components, except in a few notable cases like the creation of oxygen in the atmosphere, and of the fossil fuel reserves of oil and coal (as a result of missing biocatalysts). However, the fantastic information content of biological cells (gigabits of information in DNA alone) and the complexities of protein biochemistry for metabolism seem to place a cellular approach well beyond the current capabilities of technology, and prevent the development of intrinsically sustainable technology.

SMARTLETs: tiny shape-changing modules that collectively self-organize to larger more complex systems

A recent perspective review published in the very high impact journal Advanced Materials this month [October 2023] by researchers at the Research Center for Materials, Architectures and Integration of Nanomembranes (MAIN) of Chemnitz University of Technology, shows how a novel form of high-information-content Living Technology is now within reach, based on microrobotic electronic modules called SMARTLETs, which will soon be capable of self-assembling into complex artificial organisms. The research belongs to the new field of Microelectronic Morphogenesis, the creation of form under microelectronic control, and builds on work over the previous years at Chemnitz University of Technology to construct self-folding and self-locomoting thin film electronic modules, now carrying tiny silicon chiplets between the folds, for a massive increase in information processing capabilities. Sufficient information can now be stored in each module to encode not only complex functions but fabrication recipes (electronic genomes) for clean rooms to allow the modules to be copied and evolved like cells, but safely because of the gating of reproduction through human operated clean room facilities.

Electrical self-awareness during self-assembly

In addition, the chiplets can provide neuromorphic learning capabilities allowing them to improve performance during operation. A further key feature of the specific self-assembly of these modules, based on matching physical bar codes, is that electrical and fluidic connections can be achieved between modules. These can then be employed, to make the electronic chiplets on board “aware” of the state of assembly, and of potential errors, allowing them to direct repair, correct mis-assembly, induce disassembly and form collective functions spanning many modules. Such functions include extended communication (antennae), power harvesting and redistribution, remote sensing, material redistribution etc.

So why is this technology vital for sustainability?

The complete digital fab description for modules, for which actually only a limited number of types are required even for complex organisms, allows their material content, responsible originator and environmentally relevant exposure all to be read out. Prof. Dagmar Nuissl-Gesmann from the Law Department at Chemnitz University of Technology observes that “this fine-grained documentation of responsibility intrinsic down to microscopic scales will be a game changer in allowing legal assignment of environmental and social responsibility for our technical artefacts”.

Furthermore, the self-locomotion and self-assembly-disassembly capabilities allows the modules to self-sort for recycling. Modules can be regained, reused, reconfigured, and redeployed in different artificial organisms. If they are damaged, then their limited and documented types facilitate efficient custom recycling of materials with established and optimized protocols for these sorted and now identical entities. These capabilities complement the other more obvious advantages in terms of design development and reuse in this novel reconfigurable media. As Prof. Marlen Arnold, an expert in Sustainability of the Faculty of Economics and Business Administration observes, “Even at high volumes of deployment use, these properties could provide this technology with a hitherto unprecedented level of sustainability which would set the bar for future technologies to share our planet safely with us.”

Contribution to European Living Technology

This research is a first contribution of MAIN/Chemnitz University of Technology, as a new member of the European Centre for Living Technology ECLT, based in Venice,” says Prof. Oliver G. Schmidt, Scientific Director of the Research Center MAIN and adds that “It’s fantastic to see that our deep collaboration with ECLT is paying off so quickly with immediate transdisciplinary benefit for several scientific communities.” “Theoretical research at the ECLT has been urgently in need of novel technology systems able to implement the core properties of living systems.” comments Prof. John McCaskill, coauthor of the paper, and a grounding director of the ECLT in 2004.

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

Microelectronic Morphogenesis: Smart Materials with Electronics Assembling into Artificial Organisms by John S. McCaskill, Daniil Karnaushenko, Minshen Zhu, Oliver G. Schmidt. Advanced Materials DOI: https://doi.org/10.1002/adma.202306344 First published: 09 October 2023

This paper is open access.

A formal theory for neuromorphic (brainlike) computing hardware needed

This is one my older pieces as the information dates back to October 2023 but neuromorphic computing is one of my key interests and I’m particularly interested to see the upsurge in the discussion of hardware, here goes. From an October 17, 2023 news item on Nanowerk,

There is an intense, worldwide search for novel materials to build computer microchips with that are not based on classic transistors but on much more energy-saving, brain-like components. However, whereas the theoretical basis for classic transistor-based digital computers is solid, there are no real theoretical guidelines for the creation of brain-like computers.

Such a theory would be absolutely necessary to put the efforts that go into engineering new kinds of microchips on solid ground, argues Herbert Jaeger, Professor of Computing in Cognitive Materials at the University of Groningen [Netherlands].

Key Takeaways
Scientists worldwide are searching for new materials to build energy-saving, brain-like computer microchips as classic transistor miniaturization reaches its physical limit.

Theoretical guidelines for brain-like computers are lacking, making it crucial for advancements in the field.

The brain’s versatility and robustness serve as an inspiration, despite limited knowledge about its exact workings.

A recent paper suggests that a theory for non-digital computers should focus on continuous, analogue signals and consider the characteristics of new materials.

Bridging gaps between diverse scientific fields is vital for developing a foundational theory for neuromorphic computing..

An October 17, 2023 University of Groningen press release (also on EurekAlert), which originated the news item, provides more context for this proposal,

Computers have, so far, relied on stable switches that can be off or on, usually transistors. These digital computers are logical machines and their programming is also based on logical reasoning. For decades, computers have become more powerful by further miniaturization of the transistors, but this process is now approaching a physical limit. That is why scientists are working to find new materials to make more versatile switches, which could use more values than just the digitals 0 or 1.

Dangerous pitfall

Jaeger is part of the Groningen Cognitive Systems and Materials Center (CogniGron), which aims to develop neuromorphic (i.e. brain-like) computers. CogniGron is bringing together scientists who have very different approaches: experimental materials scientists and theoretical modelers from fields as diverse as mathematics, computer science, and AI. Working closely with materials scientists has given Jaeger a good idea of the challenges that they face when trying to come up with new computational materials, while it has also made him aware of a dangerous pitfall: there is no established theory for the use of non-digital physical effects in computing systems.

Our brain is not a logical system. We can reason logically, but that is only a small part of what our brain does. Most of the time, it must work out how to bring a hand to a teacup or wave to a colleague on passing them in a corridor. ‘A lot of the information-processing that our brain does is this non-logical stuff, which is continuous and dynamic. It is difficult to formalize this in a digital computer,’ explains Jaeger. Furthermore, our brains keep working despite fluctuations in blood pressure, external temperature, or hormone balance, and so on. How is it possible to create a computer that is as versatile and robust? Jaeger is optimistic: ‘The simple answer is: the brain is proof of principle that it can be done.’

Neurons

The brain is, therefore, an inspiration for materials scientists. Jaeger: ‘They might produce something that is made from a few hundred atoms and that will oscillate, or something that will show bursts of activity. And they will say: “That looks like how neurons work, so let’s build a neural network”.’ But they are missing a vital bit of knowledge here. ‘Even neuroscientists don’t know exactly how the brain works. This is where the lack of a theory for neuromorphic computers is problematic. Yet, the field doesn’t appear to see this.’

In a paper published in Nature Communications on 16 August, Jaeger and his colleagues Beatriz Noheda (scientific director of CogniGron) and Wilfred G. van der Wiel (University of Twente) present a sketch of what a theory for non-digital computers might look like. They propose that instead of stable 0/1 switches, the theory should work with continuous, analogue signals. It should also accommodate the wealth of non-standard nanoscale physical effects that the materials scientists are investigating.

Sub-theories

Something else that Jaeger has learned from listening to materials scientists is that devices from these new materials are difficult to construct. Jaeger: ‘If you make a hundred of them, they will not all be identical.’ This is actually very brain-like, as our neurons are not all exactly identical either. Another possible issue is that the devices are often brittle and temperature-sensitive, continues Jaeger. ‘Any theory for neuromorphic computing should take such characteristics into account.’

Importantly, a theory underpinning neuromorphic computing will not be a single theory but will be constructed from many sub-theories (see image below). Jaeger: ‘This is in fact how digital computer theory works as well, it is a layered system of connected sub-theories.’ Creating such a theoretical description of neuromorphic computers will require close collaboration of experimental materials scientists and formal theoretical modellers. Jaeger: ‘Computer scientists must be aware of the physics of all these new materials [emphasis mine] and materials scientists should be aware of the fundamental concepts in computing.’

Blind spots

Bridging this divide between materials science, neuroscience, computing science, and engineering is exactly why CogniGron was founded at the University of Groningen: it brings these different groups together. ‘We all have our blind spots,’ concludes Jaeger. ‘And the biggest gap in our knowledge is a foundational theory for neuromorphic computing. Our paper is a first attempt at pointing out how such a theory could be constructed and how we can create a common language.’

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

Toward a formal theory for computing machines made out of whatever physics offers by Herbert Jaeger, Beatriz Noheda & Wilfred G. van der Wiel. Nature Communications volume 14, Article number: 4911 (2023) DOI: https://doi.org/10.1038/s41467-023-40533-1 Published: 16 August 2023

This paper is open access and there’s a 76 pp. version, “Toward a formal theory for computing machines made out of whatever physics offers: extended version” (emphasis mine) available on arXchiv.

Caption: A general theory of physical computing systems would comprise existing theories as special cases. Figure taken from an extended version of the Nature Comm paper on arXiv. Credit: Jaeger et al. / University of Groningen

With regard to new materials for neuromorphic computing, my January 4, 2024 posting highlights a proposed quantum material for this purpose.