Tag Archives: James K. Gimzewski

A tangle of silver nanowires for brain-like action

I’ve been meaning to get to this news item from late 2019 as it features work from a team that I’ve been following for a number of years now. First mentioned here in an October 17, 2011 posting, James Gimzewski has been working with researchers at the University of California at Los Angeles (UCLA) and researchers at Japan’s National Institute for Materials Science (NIMS) on neuromorphic computing.

This particular research had a protracted rollout with the paper being published in October 2019 and the last news item about it being published in mid-December 2019.

A December 17, 2029 news item on Nanowerk was the first to alert me to this new work (Note: A link has been removed),

UCLA scientists James Gimzewski and Adam Stieg are part of an international research team that has taken a significant stride toward the goal of creating thinking machines.

Led by researchers at Japan’s National Institute for Materials Science, the team created an experimental device that exhibited characteristics analogous to certain behaviors of the brain — learning, memorization, forgetting, wakefulness and sleep. The paper, published in Scientific Reports (“Emergent dynamics of neuromorphic nanowire networks”), describes a network in a state of continuous flux.

A December 16, 2019 UCLA news release, which originated the news item, offers more detail (Note: A link has been removed),

“This is a system between order and chaos, on the edge of chaos,” said Gimzewski, a UCLA distinguished professor of chemistry and biochemistry, a member of the California NanoSystems Institute at UCLA and a co-author of the study. “The way that the device constantly evolves and shifts mimics the human brain. It can come up with different types of behavior patterns that don’t repeat themselves.”

The research is one early step along a path that could eventually lead to computers that physically and functionally resemble the brain — machines that may be capable of solving problems that contemporary computers struggle with, and that may require much less power than today’s computers do.

The device the researchers studied is made of a tangle of silver nanowires — with an average diameter of just 360 nanometers. (A nanometer is one-billionth of a meter.) The nanowires were coated in an insulating polymer about 1 nanometer thick. Overall, the device itself measured about 10 square millimeters — so small that it would take 25 of them to cover a dime.

Allowed to randomly self-assemble on a silicon wafer, the nanowires formed highly interconnected structures that are remarkably similar to those that form the neocortex, the part of the brain involved with higher functions such as language, perception and cognition.

One trait that differentiates the nanowire network from conventional electronic circuits is that electrons flowing through them cause the physical configuration of the network to change. In the study, electrical current caused silver atoms to migrate from within the polymer coating and form connections where two nanowires overlap. The system had about 10 million of these junctions, which are analogous to the synapses where brain cells connect and communicate.

The researchers attached two electrodes to the brain-like mesh to profile how the network performed. They observed “emergent behavior,” meaning that the network displayed characteristics as a whole that could not be attributed to the individual parts that make it up. This is another trait that makes the network resemble the brain and sets it apart from conventional computers.

After current flowed through the network, the connections between nanowires persisted for as much as one minute in some cases, which resembled the process of learning and memorization in the brain. Other times, the connections shut down abruptly after the charge ended, mimicking the brain’s process of forgetting.

In other experiments, the research team found that with less power flowing in, the device exhibited behavior that corresponds to what neuroscientists see when they use functional MRI scanning to take images of the brain of a sleeping person. With more power, the nanowire network’s behavior corresponded to that of the wakeful brain.

The paper is the latest in a series of publications examining nanowire networks as a brain-inspired system, an area of research that Gimzewski helped pioneer along with Stieg, a UCLA research scientist and an associate director of CNSI.

“Our approach may be useful for generating new types of hardware that are both energy-efficient and capable of processing complex datasets that challenge the limits of modern computers,” said Stieg, a co-author of the study.

The borderline-chaotic activity of the nanowire network resembles not only signaling within the brain but also other natural systems such as weather patterns. That could mean that, with further development, future versions of the device could help model such complex systems.

In other experiments, Gimzewski and Stieg already have coaxed a silver nanowire device to successfully predict statistical trends in Los Angeles traffic patterns based on previous years’ traffic data.

Because of their similarities to the inner workings of the brain, future devices based on nanowire technology could also demonstrate energy efficiency like the brain’s own processing. The human brain operates on power roughly equivalent to what’s used by a 20-watt incandescent bulb. By contrast, computer servers where work-intensive tasks take place — from training for machine learning to executing internet searches — can use the equivalent of many households’ worth of energy, with the attendant carbon footprint.

“In our studies, we have a broader mission than just reprogramming existing computers,” Gimzewski said. “Our vision is a system that will eventually be able to handle tasks that are closer to the way the human being operates.”

The study’s first author, Adrian Diaz-Alvarez, is from the International Center for Material Nanoarchitectonics at Japan’s National Institute for Materials Science. Co-authors include Tomonobu Nakayama and Rintaro Higuchi, also of NIMS; and Zdenka Kuncic at the University of Sydney in Australia.

Caption: (a) Micrograph of the neuromorphic network fabricated by this research team. The network contains of numerous junctions between nanowires, which operate as synaptic elements. When voltage is applied to the network (between the green probes), current pathways (orange) are formed in the network. (b) A Human brain and one of its neuronal networks. The brain is known to have a complex network structure and to operate by means of electrical signal propagation across the network. Credit: NIMS

A November 11, 2019 National Institute for Materials Science (Japan) press release (also on EurekAlert but dated December 25, 2019) first announced the news,

An international joint research team led by NIMS succeeded in fabricating a neuromorphic network composed of numerous metallic nanowires. Using this network, the team was able to generate electrical characteristics similar to those associated with higher order brain functions unique to humans, such as memorization, learning, forgetting, becoming alert and returning to calm. The team then clarified the mechanisms that induced these electrical characteristics.

The development of artificial intelligence (AI) techniques has been rapidly advancing in recent years and has begun impacting our lives in various ways. Although AI processes information in a manner similar to the human brain, the mechanisms by which human brains operate are still largely unknown. Fundamental brain components, such as neurons and the junctions between them (synapses), have been studied in detail. However, many questions concerning the brain as a collective whole need to be answered. For example, we still do not fully understand how the brain performs such functions as memorization, learning and forgetting, and how the brain becomes alert and returns to calm. In addition, live brains are difficult to manipulate in experimental research. For these reasons, the brain remains a “mysterious organ.” A different approach to brain research?in which materials and systems capable of performing brain-like functions are created and their mechanisms are investigated?may be effective in identifying new applications of brain-like information processing and advancing brain science.

The joint research team recently built a complex brain-like network by integrating numerous silver (Ag) nanowires coated with a polymer (PVP) insulating layer approximately 1 nanometer in thickness. A junction between two nanowires forms a variable resistive element (i.e., a synaptic element) that behaves like a neuronal synapse. This nanowire network, which contains a large number of intricately interacting synaptic elements, forms a “neuromorphic network”. When a voltage was applied to the neuromorphic network, it appeared to “struggle” to find optimal current pathways (i.e., the most electrically efficient pathways). The research team measured the processes of current pathway formation, retention and deactivation while electric current was flowing through the network and found that these processes always fluctuate as they progress, similar to the human brain’s memorization, learning, and forgetting processes. The observed temporal fluctuations also resemble the processes by which the brain becomes alert or returns to calm. Brain-like functions simulated by the neuromorphic network were found to occur as the huge number of synaptic elements in the network collectively work to optimize current transport, in the other words, as a result of self-organized and emerging dynamic processes..

The research team is currently developing a brain-like memory device using the neuromorphic network material. The team intends to design the memory device to operate using fundamentally different principles than those used in current computers. For example, while computers are currently designed to spend as much time and electricity as necessary in pursuit of absolutely optimum solutions, the new memory device is intended to make a quick decision within particular limits even though the solution generated may not be absolutely optimum. The team also hopes that this research will facilitate understanding of the brain’s information processing mechanisms.

This project was carried out by an international joint research team led by Tomonobu Nakayama (Deputy Director, International Center for Materials Nanoarchitectonics (WPI-MANA), NIMS), Adrian Diaz Alvarez (Postdoctoral Researcher, WPI-MANA, NIMS), Zdenka Kuncic (Professor, School of Physics, University of Sydney, Australia) and James K. Gimzewski (Professor, California NanoSystems Institute, University of California Los Angeles, USA).

Here at last is a link to and a citation for the paper,

Emergent dynamics of neuromorphic nanowire networks by Adrian Diaz-Alvarez, Rintaro Higuchi, Paula Sanz-Leon, Ido Marcus, Yoshitaka Shingaya, Adam Z. Stieg, James K. Gimzewski, Zdenka Kuncic & Tomonobu Nakayama. Scientific Reports volume 9, Article number: 14920 (2019) DOI: https://doi.org/10.1038/s41598-019-51330-6 Published: 17 October 2019

This paper is open access.

New nanomapping technology: CRISPR-CAS9 as a programmable nanoparticle

A November 21, 2017 news item on Nanowerk describes a rather extraordinary (to me, anyway) approach to using CRRISP ( Clustered Regularly Interspaced Short Palindromic Repeats)-CAS9 (Note: A link has been removed),

A team of scientists led by Virginia Commonwealth University physicist Jason Reed, Ph.D., have developed new nanomapping technology that could transform the way disease-causing genetic mutations are diagnosed and discovered. Described in a study published today [November 21, 2017] in the journal Nature Communications (“DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle”), this novel approach uses high-speed atomic force microscopy (AFM) combined with a CRISPR-based chemical barcoding technique to map DNA nearly as accurately as DNA sequencing while processing large sections of the genome at a much faster rate. What’s more–the technology can be powered by parts found in your run-of-the-mill DVD player.

A November 21, 2017 Virginia Commonwealth University news release by John Wallace, which originated the news item, provides more detail,

The human genome is made up of billions of DNA base pairs. Unraveled, it stretches to a length of nearly six feet long. When cells divide, they must make a copy of their DNA for the new cell. However, sometimes various sections of the DNA are copied incorrectly or pasted together at the wrong location, leading to genetic mutations that cause diseases such as cancer. DNA sequencing is so precise that it can analyze individual base pairs of DNA. But in order to analyze large sections of the genome to find genetic mutations, technicians must determine millions of tiny sequences and then piece them together with computer software. In contrast, biomedical imaging techniques such as fluorescence in situ hybridization, known as FISH, can only analyze DNA at a resolution of several hundred thousand base pairs.

Reed’s new high-speed AFM method can map DNA to a resolution of tens of base pairs while creating images up to a million base pairs in size. And it does it using a fraction of the amount of specimen required for DNA sequencing.

“DNA sequencing is a powerful tool, but it is still quite expensive and has several technological and functional limitations that make it difficult to map large areas of the genome efficiently and accurately,” said Reed, principal investigator on the study. Reed is a member of the Cancer Molecular Genetics research program at VCU Massey Cancer Center and an associate professor in the Department of Physics in the College of Humanities and Sciences.

“Our approach bridges the gap between DNA sequencing and other physical mapping techniques that lack resolution,” he said. “It can be used as a stand-alone method or it can complement DNA sequencing by reducing complexity and error when piecing together the small bits of genome analyzed during the sequencing process.”

IBM scientists made headlines in 1989 when they developed AFM technology and used a related technique to rearrange molecules at the atomic level to spell out “IBM.” AFM achieves this level of detail by using a microscopic stylus — similar to a needle on a record player — that barely makes contact with the surface of the material being studied. The interaction between the stylus and the molecules creates the image. However, traditional AFM is too slow for medical applications and so it is primarily used by engineers in materials science.

“Our device works in the same fashion as AFM but we move the sample past the stylus at a much greater velocity and use optical instruments to detect the interaction between the stylus and the molecules. We can achieve the same level of detail as traditional AFM but can process material more than a thousand times faster,” said Reed, whose team proved the technology can be mainstreamed by using optical equipment found in DVD players. “High-speed AFM is ideally suited for some medical applications as it can process materials quickly and provide hundreds of times more resolution than comparable imaging methods.”

Increasing the speed of AFM was just one hurdle Reed and his colleagues had to overcome. In order to actually identify genetic mutations in DNA, they had to develop a way to place markers or labels on the surface of the DNA molecules so they could recognize patterns and irregularities. An ingenious chemical barcoding solution was developed using a form of CRISPR technology.

CRISPR has made a lot of headlines recently in regard to gene editing. CRISPR is an enzyme that scientists have been able to “program” using targeting RNA in order to cut DNA at precise locations that the cell then repairs on its own. Reed’s team altered the chemical reaction conditions of the CRISPR enzyme so that it only sticks to the DNA and does not actually cut it.

“Because the CRISPR enzyme is a protein that’s physically bigger than the DNA molecule, it’s perfect for this barcoding application,” Reed said. “We were amazed to discover this method is nearly 90 percent efficient at bonding to the DNA molecules. And because it’s easy to see the CRISPR proteins, you can spot genetic mutations among the patterns in DNA.”

To demonstrate the technique’s effectiveness, the researchers mapped genetic translocations present in lymph node biopsies of lymphoma patients. Translocations occur when one section of the DNA gets copied and pasted to the wrong place in the genome. They are especially prevalent in blood cancers such as lymphoma but occur in other cancers as well.

While there are many potential uses for this technology, Reed and his team are focusing on medical applications. They are currently developing software based on existing algorithms that can analyze patterns in sections of DNA up to and over a million base pairs in size. Once completed, it would not be hard to imagine this shoebox-sized instrument in pathology labs assisting in the diagnosis and treatment of diseases linked to genetic mutations.

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

DNA nanomapping using CRISPR-Cas9 as a programmable nanoparticle by Andrey Mikheikin, Anita Olsen, Kevin Leslie, Freddie Russell-Pavier, Andrew Yacoot, Loren Picco, Oliver Payton, Amir Toor, Alden Chesney, James K. Gimzewski, Bud Mishra, & Jason Reed. Nature Communications 8, Article number: 1665 (2017) doi:10.1038/s41467-017-01891-9 Published online: 21 November 2017

This paper is open access.

Synaptic electronics

There’s been a lot about the memristor, being developed at HP Labs, at the University of Michigan, and elsewhere, on this blog and significantly less on other approaches to creating nanodevices with neuromorphic properties by researchers in Japan and in the US. The Dec. 20, 2012 news item on ScienceDaily notes,

Researchers in Japan and the US propose a nanoionic device with a range of neuromorphic and electrical multifunctions that may allow the fabrication of on-demand configurable circuits, analog memories and digital-neural fused networks in one device architecture.

… Now Rui Yang, Kazuya Terabe and colleagues at the National Institute for Materials Science in Japan and the University of California, Los Angeles, in the US have developed two-, three-terminal WO3-x-based nanoionic devices capable of a broad range of neuromorphic and electrical functions.

The originating Dec. 20, 2012 news release from Japan’s International Center for Materials draws a parallel between the device’s properties and neural behaviour,  explains the ‘why’ of the process, and mentions what applications the researchers believe could be developed,

The researchers draw similarities between the device properties — volatile and non-volatile states and the current fading process following positive voltage pulses — with models for neural behaviour —that is, short- and long-term memory and forgetting processes. They explain the behaviour as the result of oxygen ions migrating within the device in response to the voltage sweeps. Accumulation of the oxygen ions at the electrode leads to Schottky-like potential barriers and the resulting changes in resistance and rectifying characteristics. The stable bipolar switching behaviour at the Pt/WO3-x interface is attributed to the formation of the electric conductive filament and oxygen absorbability of the Pt electrode.

As the researchers conclude, “These capabilities open a new avenue for circuits, analog memories, and artificially fused digital neural networks using on-demand programming by input pulse polarity, magnitude, and repetition history.”

For those who wish to delve more deeply, here’s the citation (from the ScienceDaily news item),

Rui Yang, Kazuya Terabe, Guangqiang Liu, Tohru Tsuruoka, Tsuyoshi Hasegawa, James K. Gimzewski, Masakazu Aono. On-Demand Nanodevice with Electrical and Neuromorphic Multifunction Realized by Local Ion Migration. ACS Nano, 2012; 6 (11): 9515 DOI: 10.1021/nn302510e

The news release does not state explicitly why this would be considered an on-demand device. The article is behind a paywall.

There was a recent attempt to mimic brain processing not based in nanoelectronics but on mimicking brain activity by creating virtual neurons. A Canadian team at the University of Waterloo led by Chris Eliasmith made a sensation  with SPAUN (Semantic Pointer Architecture Unified Network) in late Nov. 2012 (mentioned in my Nov. 29, 2012 posting).