Amazing Science

While digital technology is extremely good at solving certain problems, it often struggles with tasks that the human brain excels at. In a new study, scientists have leveraged brain-inspired connectivity between artificial neurons to solve a real-world problem of identifying mutations of a new viral species.

In the September issue of the journal Nature, scientists from Texas A&M University, Hewlett Packard Labs and Stanford University have described a new nanodevice that acts almost identically to a brain cell. Furthermore, they have shown that these synthetic brain cells can be joined together to form intricate networks that can then solve problems in a brain-like manner.

"This is the first study where we have been able to emulate a neuron with just a single nanoscale device, which would otherwise need hundreds of transistors," said Dr. R. Stanley Williams, senior author on the study and professor in the Department of Electrical and Computer Engineering. "We have also been able to successfully use networks of our artificial neurons to solve toy versions of a real-world problem that is computationally intense even for the most sophisticated digital technologies."

In particular, the researchers have demonstrated proof of concept that their brain-inspired system can identify possible mutations in a virus, which is highly relevant for ensuring the efficacy of vaccines and medications for strains exhibiting genetic diversity.

Over the past decades, digital technologies have become smaller and faster largely because of the advancements in transistor technology. However, these critical circuit components are fast approaching their limit of how small they can be built, initiating a global effort to find a new type of technology that can supplement, if not replace, transistors.

In addition to this "scaling-down" problem, transistor-based digital technologies have other well-known challenges. For example, they struggle at finding optimal solutions when presented with large sets of data. "Let's take a familiar example of finding the shortest route from your office to your home. If you have to make a single stop, it's a fairly easy problem to solve. But if for some reason you need to make 15 stops in between, you have 43 billion routes to choose from," said Dr. Suhas Kumar, lead author on the study and researcher at Hewlett Packard Labs. "This is now an optimization problem, and current computers are rather inept at solving it."

Kumar added that another arduous task for digital machines is pattern recognition, such as identifying a face as the same regardless of viewpoint or recognizing a familiar voice buried within a din of sounds. But tasks that can send digital machines into a computational tizzy are ones at which the brain excels. In fact, brains are not just quick at recognition and optimization problems, but they also consume far less energy than digital systems. Hence, by mimicking how the brain solves these types of tasks, Williams said brain-inspired or neuromorphic systems could potentially overcome some of the computational hurdles faced by current digital technologies.

To build the fundamental building block of the brain or a neuron, the researchers assembled a synthetic nanoscale device consisting of layers of different inorganic materials, each with a unique function. However, they said the real magic happens in the thin layer made of the compound niobium dioxide.

When a small voltage is applied to this region, its temperature begins to increase. But when the temperature reaches a critical value, niobium dioxide undergoes a quick change in personality, turning from an insulator to a conductor. But as it begins to conduct electric currents, its temperature drops and niobium dioxide switches back to being an insulator.

These back-and-forth transitions enable the synthetic devices to generate a pulse of electrical current that closely resembles the profile of electrical spikes, or action potentials, produced by biological neurons. Further, by changing the voltage across their synthetic neurons, the researchers reproduced a rich range of neuronal behaviors observed in the brain, such as sustained, burst and chaotic firing of electrical spikes. "Capturing the dynamical behavior of neurons is a key goal for brain-inspired computers," said Kumar. "Altogether, we were able to recreate around 15 types of neuronal firing profiles, all using a single electrical component and at much lower energies compared to transistor-based circuits."

The discovery power of the gene chip is coming to nanotechnology, as a Northwestern University research team develops a  tool to rapidly test millions — and perhaps even billions — of different nanoparticles at one time to zero in on the best nanoparticle for a specific use.

When materials are miniaturized, their properties — optical, structural, electrical, mechanical and chemical — change, offering new possibilities. But determining what nanoparticle size and composition are best for a given application, such as catalysts, biodiagnostic labels, pharmaceuticals and electronic devices, is a daunting task.

“As scientists, we’ve only just begun to investigate what materials can be made on the nanoscale,” said Northwestern’s Chad A. Mirkin, a world leader in nanotechnology research and its application, who led the study. “Screening a million potentially useful nanoparticles, for example, could take several lifetimes. Once optimized, our tool will enable researchers to pick the winner much faster than conventional methods. We have the ultimate discovery tool.”

Combinatorial libraries of nanoparticles - more than half never existed on Earth.

Using a Northwestern technique that deposits materials on a surface, Mirkin and his team figured out how to make combinatorial libraries of nanoparticles in a controlled way. (A combinatorial library is a collection of systematically varied structures encoded at specific sites on a surface.) Their study was published today (June 24) by the journal Science.

The nanoparticle libraries are much like a gene chip, Mirkin says, where thousands of different spots of DNA are used to identify the presence of a disease or toxin. Thousands of reactions can be done simultaneously, providing results in just a few hours. Similarly, Mirkin and his team’s libraries will enable scientists to rapidly make and screen millions to billions of nanoparticles of different compositions and sizes for desirable physical and chemical properties.

“The ability to make libraries of nanoparticles will open a new field of nanocombinatorics, where size — on a scale that matters — and composition become tunable parameters,” Mirkin said. “This is a powerful approach to discovery science.”

Mirkin is the George B. Rathmann Professor of Chemistry in the Weinberg College of Arts and Sciences and founding director of Northwestern’s International Institute for Nanotechnology.

Using just five metallic elements — gold, silver, cobalt, copper and nickel — Mirkin and his team developed an array of unique structures by varying every elemental combination. In previous work, the researchers had shown that particle diameter also can be varied deliberately on the 1- to 100-nanometer length scale.

Researchers from the National Institute of Standards and Technology (NIST) and collaborators have proposed a design for the first DNA sequencer based on an electronic nanosensor that can detect tiny motions as small as a single atom.

The proposed device—a type of capacitor, which stores electric charge—is a tiny ribbon of molybdenum disulfide suspended over a metal electrode and immersed in water. The ribbon is 15.5 nm long and 4.5 nm wide. Single-stranded DNA, containing a chain of bases (bits of genetic code), is threaded through a hole 2.5 nm wide in the thin ribbon. The ribbon flexes only when a DNA base pairs up with and then separates from a complementary base affixed to the hole. The membrane motion is detected as an electrical signal.

As described in a new paper, the NIST team made numerical simulations and theoretical estimates to show the membrane would be 79 to 86 percent accurate in identifying DNA bases in a single measurement at speeds up to about 70 million bases per second. Integrated circuits would detect and measure electrical signals and identify bases. The results suggest such a device could be a fast, accurate and cost-effective DNA sequencer, according to the paper.

Conventional sequencing, developed in the 1970s, involves separating, copying, labeling and reassembling pieces of DNA to read the genetic information. Newer methods include automated sequencing of many DNA fragments at once—still costly—and novel "nanopore sequencing" concepts. For example, the same NIST group recently demonstrated the idea of sequencing DNA by passing it through a graphene nanopore, and measuring how graphene's electronic properties respond to strain.

The latest NIST proposal relies on a thin film of molybdenum disulfide—a stable, layered material that conducts electricity and is often used as a lubricant. Among other advantages, this material does not stick to DNA, which can be a problem with graphene. The NIST team suggests the method might even work without a nanopore—a simpler design—by passing DNA across the edge of the membrane.

"This approach potentially solves the issue with DNA sticking to graphene if inserted improperly, because this approach does not use graphene, period," NIST theorist and lead author Alex Smolyanitsky said. "Another major difference is that instead of relying on the properties of graphene or any particular material used, we read motions electrically in an easier way by forming a capacitor. This makes any electrically conductive membrane suitable for the application."