Tag Archives: human brain

Kempner Institute for the Study of Natural and Artificial Intelligence launched at Harvard University and University of Manchester pushes the boundaries of smart robotics and AI

Before getting to the two news items, it might be a good idea to note that ‘artificial intelligence (AI)’ and ‘robot’ are not synonyms although they are often used that way, even by people who should know better. (sigh … I do it too)

A robot may or may not be animated with artificial intelligence while artificial intelligence algorithms may be installed on a variety of devices such as a phone or a computer or a thermostat or a … .

It’s something to bear in mind when reading about the two new institutions being launched. Now, on to Harvard University.

Kempner Institute for the Study of Natural and Artificial Intelligence

A September 23, 2022 Chan Zuckerberg Initiative (CZI) news release (also on EurekAlert) announces a symposium to launch a new institute close to Mark Zuckerberg’s heart,

On Thursday [September 22, 2022], leadership from the Chan Zuckerberg Initiative (CZI) and Harvard University celebrated the launch of the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University with a symposium on Harvard’s campus. Speakers included CZI Head of Science Stephen Quake, President of Harvard University Lawrence Bacow, Provost of Harvard University Alan Garber, and Kempner Institute co-directors Bernardo Sabatini and Sham Kakade. The event also included remarks and panels from industry leaders in science, technology, and artificial intelligence, including Bill Gates, Eric Schmidt, Andy Jassy, Daniel Huttenlocher, Sam Altman, Joelle Pineau, Sangeeta Bhatia, and Yann LeCun, among many others.

The Kempner Institute will seek to better understand the basis of intelligence in natural and artificial systems. Its bold premise is that the two fields are intimately interconnected; the next generation of AI will require the same principles that our brains use for fast, flexible natural reasoning, and understanding how our brains compute and reason requires theories developed for AI. The Kempner Institute will study AI systems, including artificial neural networks, to develop both principled theories [emphasis mine] and a practical understanding of how these systems operate and learn. It will also focus on research topics such as learning and memory, perception and sensation, brain function, and metaplasticity. The Institute will recruit and train future generations of researchers from undergraduates and graduate students to post-docs and faculty — actively recruiting from underrepresented groups at every stage of the pipeline — to study intelligence from biological, cognitive, engineering, and computational perspectives.

CZI Co-Founder and Co-CEO Mark Zuckerberg [chairman and chief executive officer of Meta/Facebook] said: “The Kempner Institute will be a one-of-a-kind institute for studying intelligence and hopefully one that helps us discover what intelligent systems really are, how they work, how they break and how to repair them. There’s a lot of exciting implications because once you understand how something is supposed to work and how to repair it once it breaks, you can apply that to the broader mission the Chan Zuckerberg Initiative has to empower scientists to help cure, prevent or manage all diseases.”

CZI Co-Founder and Co-CEO Priscilla Chan said: “Just attending this school meant the world to me. But to stand on this stage and to be able to give something back is truly a dream come true … All of this progress starts with building one fundamental thing: a Kempner community that’s diverse, multi-disciplinary and multi-generational, because incredible ideas can come from anyone. If you bring together people from all different disciplines to look at a problem and give them permission to articulate their perspective, you might start seeing insights or solutions in a whole different light. And those new perspectives lead to new insights and discoveries and generate new questions that can lead an entire field to blossom. So often, that momentum is what breaks the dam and tears down old orthodoxies, unleashing new floods of new ideas that allow us to progress together as a society.”

CZI Head of Science Stephen Quake said: “It’s an honor to partner with Harvard in building this extraordinary new resource for students and science. This is a once-in-a-generation moment for life sciences and medicine. We are living in such an extraordinary and exciting time for science. Many breakthrough discoveries are going to happen not only broadly but right here on this campus and at this institute.”

CZI’s 10-year vision is to advance research and develop technologies to observe, measure, and analyze any biological process within the human body — across spatial scales and in real time. CZI’s goal is to accelerate scientific progress by funding scientific research to advance entire fields; working closely with scientists and engineers at partner institutions like the Chan Zuckerberg Biohub and Chan Zuckerberg Institute for Advanced Biological Imaging to do the research that can’t be done in conventional environments; and building and democratizing next-generation software and hardware tools to drive biological insights and generate more accurate and biologically important sources of data.

President of Harvard University Lawrence Bacow said: “Here we are with this incredible opportunity that Priscilla Chan and Mark Zuckerberg have given us to imagine taking what we know about the brain, neuroscience and how to model intelligence and putting them together in ways that can inform both, and can truly advance our understanding of intelligence from multiple perspectives.”

Kempner Institute Co-Director and Gordon McKay Professor of Computer Science and of Statistics at the Harvard John A. Paulson School of Engineering and Applied Sciences Sham Kakade said: “Now we begin assembling a world-leading research and educational program at Harvard that collectively tries to understand the fundamental mechanisms of intelligence and seeks to apply these new technologies for the benefit of humanity … We hope to create a vibrant environment for all of us to engage in broader research questions … We want to train the next generation of leaders because those leaders will go on to do the next set of great things.”

Kempner Institute Co-Director and the Alice and Rodman W. Moorhead III Professor of Neurobiology at Harvard Medical School Bernardo Sabatini said: “We’re blending research, education and computation to nurture, raise up and enable any scientist who is interested in unraveling the mysteries of the brain. This field is a nascent and interdisciplinary one, so we’re going to have to teach neuroscience to computational biologists, who are going to have to teach machine learning to cognitive scientists and math to biologists. We’re going to do whatever is necessary to help each individual thrive and push the field forward … Success means we develop mathematical theories that explain how our brains compute and learn, and these theories should be specific enough to be testable and useful enough to start to explain diseases like schizophrenia, dyslexia or autism.”

About the Chan Zuckerberg Initiative

The Chan Zuckerberg Initiative was founded in 2015 to help solve some of society’s toughest challenges — from eradicating disease and improving education, to addressing the needs of our communities. Through collaboration, providing resources and building technology, our mission is to help build a more inclusive, just and healthy future for everyone. For more information, please visit chanzuckerberg.com.

Principled theories, eh. I don’t see a single mention of ethicists or anyone in the social sciences or the humanities or the arts. How are scientists and engineers who have no training in or education in or, even, an introduction to ethics or social impacts or psychology going to manage this?

Mark Zuckerberg’s approach to these issues was something along the lines of “it’s easier to ask for forgiveness than to ask for permission.” I understand there have been changes but it took far too long to recognize the damage let alone attempt to address it.

If you want to gain a little more insight into the Kempner Institute, there’s a December 7, 2021 article by Alvin Powell announcing the institute for the Harvard Gazette,

The institute will be funded by a $500 million gift from Priscilla Chan and Mark Zuckerberg, which was announced Tuesday [December 7, 2021] by the Chan Zuckerberg Initiative. The gift will support 10 new faculty appointments, significant new computing infrastructure, and resources to allow students to flow between labs in pursuit of ideas and knowledge. The institute’s name honors Zuckerberg’s mother, Karen Kempner Zuckerberg, and her parents — Zuckerberg’s grandparents — Sidney and Gertrude Kempner. Chan and Zuckerberg have given generously to Harvard in the past, supporting students, faculty, and researchers in a range of areas, including around public service, literacy, and cures.

“The Kempner Institute at Harvard represents a remarkable opportunity to bring together approaches and expertise in biological and cognitive science with machine learning, statistics, and computer science to make real progress in understanding how the human brain works to improve how we address disease, create new therapies, and advance our understanding of the human body and the world more broadly,” said President Larry Bacow.

Q&A

Bernardo Sabatini and Sham Kakade [Institute co-directors]

GAZETTE: Tell me about the new institute. What is its main reason for being?

SABATINI: The institute is designed to take from two fields and bring them together, hopefully to create something that’s essentially new, though it’s been tried in a couple of places. Imagine that you have over here cognitive scientists and neurobiologists who study the human brain, including the basic biological mechanisms of intelligence and decision-making. And then over there, you have people from computer science, from mathematics and statistics, who study artificial intelligence systems. Those groups don’t talk to each other very much.

We want to recruit from both populations to fill in the middle and to create a new population, through education, through graduate programs, through funding programs — to grow from academic infancy — those equally versed in neuroscience and in AI systems, who can be leaders for the next generation.

Over the millions of years that vertebrates have been evolving, the human brain has developed specializations that are fundamental for learning and intelligence. We need to know what those are to understand their benefits and to ask whether they can make AI systems better. At the same time, as people who study AI and machine learning (ML) develop mathematical theories as to how those systems work and can say that a network of the following structure with the following properties learns by calculating the following function, then we can take those theories and ask, “Is that actually how the human brain works?”

KAKADE: There’s a question of why now? In the technological space, the advancements are remarkable even to me, as a researcher who knows how these things are being made. I think there’s a long way to go, but many of us feel that this is the right time to study intelligence more broadly. You might also ask: Why is this mission unique and why is this institute different from what’s being done in academia and in industry? Academia is good at putting out ideas. Industry is good at turning ideas into reality. We’re in a bit of a sweet spot. We have the scale to study approaches at a very different level: It’s not going to be just individual labs pursuing their own ideas. We may not be as big as the biggest companies, but we can work on the types of problems that they work on, such as having the compute resources to work on large language models. Industry has exciting research, but the spectrum of ideas produced is very different, because they have different objectives.

For the die-hards, there’s a September 23, 2022 article by Clea Simon in Harvard Gazette, which updates the 2021 story,

Next, Manchester, England.

Manchester Centre for Robotics and AI

Robotots take a break at a lab at The University of Manchester – picture courtesy of Marketing Manchester [downloaded from https://www.manchester.ac.uk/discover/news/manchester-ai-summit-aims-to-attract-experts-in-advanced-engineering-and-robotics/]

A November 22, 2022 University of Manchester press release (also on EurekAlert) announces both a meeting and a new centre, Note: Links to the Centre have been retained; all others have been removed,

How humans and super smart robots will live and work together in the future will be among the key issues being scrutinised by experts at a new centre of excellence for AI and autonomous machines based at The University of Manchester.

The Manchester Centre for Robotics and AI will be a new specialist multi-disciplinary centre to explore developments in smart robotics through the lens of artificial intelligence (AI) and autonomous machinery.

The University of Manchester has built a modern reputation of excellence in AI and robotics, partly based on the legacy of pioneering thought leadership begun in this field in Manchester by legendary codebreaker Alan Turing.

Manchester’s new multi-disciplinary centre is home to world-leading research from across the academic disciplines – and this group will hold its first conference on Wednesday, Nov 23, at the University’s new engineering and materials facilities.

A  highlight will be a joint talk by robotics expert Dr Andy Weightman and theologian Dr Scott Midson which is expected to put a spotlight on ‘posthumanism’, a future world where humans won’t be the only highly intelligent decision-makers.

Dr Weightman, who researches home-based rehabilitation robotics for people with neurological impairment, and Dr Midson, who researches theological and philosophical critiques of posthumanism, will discuss how interdisciplinary research can help with the special challenges of rehabilitation robotics – and, ultimately, what it means to be human “in the face of the promises and challenges of human enhancement through robotic and autonomous machines”.

Other topics that the centre will have a focus on will include applications of robotics in extreme environments.

For the past decade, a specialist Manchester team led by Professor Barry Lennox has designed robots to work safely in nuclear decommissioning sites in the UK. A ground-breaking robot called Lyra that has been developed by Professor Lennox’s team – and recently deployed at the Dounreay site in Scotland, the “world’s deepest nuclear clean up site” – has been listed in Time Magazine’s Top 200 innovations of 2022.

Angelo Cangelosi, Professor of Machine Learning and Robotics at Manchester, said the University offers a world-leading position in the field of autonomous systems – a technology that will be an integral part of our future world. 

Professor Cangelosi, co-Director of Manchester’s Centre for Robotics and AI, said: “We are delighted to host our inaugural conference which will provide a special showcase for our diverse academic expertise to design robotics for a variety of real world applications.

“Our research and innovation team are at the interface between robotics, autonomy and AI – and their knowledge is drawn from across the University’s disciplines, including biological and medical sciences – as well the humanities and even theology. [emphases mine]

“This rich diversity offers Manchester a distinctive approach to designing robots and autonomous systems for real world applications, especially when combined with our novel use of AI-based knowledge.”

Delegates will have a chance to observe a series of robots and autonomous machines being demoed at the new conference.

The University of Manchester’s Centre for Robotics and AI will aim to: 

  • design control systems with a focus on bio-inspired solutions to mechatronics, eg the use of biomimetic sensors, actuators and robot platforms; 
  • develop new software engineering and AI methodologies for verification in autonomous systems, with the aim to design trustworthy autonomous systems; 
  • research human-robot interaction, with a pioneering focus on the use of brain-inspired approaches [emphasis mine] to robot control, learning and interaction; and 
  • research the ethics and human-centred robotics issues, for the understanding of the impact of the use of robots and autonomous systems with individuals and society. 

In some ways, the Kempner Institute and the Manchester Centre for Robotics and AI have very similar interests, especially where the brain is concerned. What fascinates me is the Manchester Centre’s inclusion of theologian Dr Scott Midson and the discussion (at the meeting) of ‘posthumanism’. The difference is between actual engagement at the symposium (the centre) and mere mention in a news release (the institute).

I wish the best for both institutions.

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.

Using light to manipulate neurons

There are three (or more?) possible applications including neuromorphic computing for this new optoelectronic technology which is based on black phophorus. A July 16, 2019 news item on Nanowerk announces the research,

Researchers from RMIT University [Australia] drew inspiration from an emerging tool in biotechnology – optogenetics – to develop a device that replicates the way the brain stores and loses information.

Optogenetics allows scientists to delve into the body’s electrical system with incredible precision, using light to manipulate neurons so that they can be turned on or off.

The new chip is based on an ultra-thin material that changes electrical resistance in response to different wavelengths of light, enabling it to mimic the way that neurons work to store and delete information in the brain.

Caption: The new chip is based on an ultra-thin material that changes electrical resistance in response to different wavelengths of light. Credit: RMIT University

A July 17, 2019 RMIT University press release (also on EurekAlert but published on July 16, 2019), which originated the news item, expands on the theme,

Research team leader Dr Sumeet Walia said the technology moves us closer towards artificial intelligence (AI) that can harness the brain’s full sophisticated functionality.

“Our optogenetically-inspired chip imitates the fundamental biology of nature’s best computer – the human brain,” Walia said.

“Being able to store, delete and process information is critical for computing, and the brain does this extremely efficiently.

“We’re able to simulate the brain’s neural approach simply by shining different colours onto our chip.

“This technology takes us further on the path towards fast, efficient and secure light-based computing.

“It also brings us an important step closer to the realisation of a bionic brain – a brain-on-a-chip that can learn from its environment just like humans do.”

Dr Taimur Ahmed, lead author of the study published in Advanced Functional Materials, said being able to replicate neural behavior on an artificial chip offered exciting avenues for research across sectors.

“This technology creates tremendous opportunities for researchers to better understand the brain and how it’s affected by disorders that disrupt neural connections, like Alzheimer’s disease and dementia,” Ahmed said.

The researchers, from the Functional Materials and Microsystems Research Group at RMIT, have also demonstrated the chip can perform logic operations – information processing – ticking another box for brain-like functionality.

Developed at RMIT’s MicroNano Research Facility, the technology is compatible with existing electronics and has also been demonstrated on a flexible platform, for integration into wearable electronics.

How the chip works:

Neural connections happen in the brain through electrical impulses. When tiny energy spikes reach a certain threshold of voltage, the neurons bind together – and you’ve started creating a memory.

On the chip, light is used to generate a photocurrent. Switching between colors causes the current to reverse direction from positive to negative.

This direction switch, or polarity shift, is equivalent to the binding and breaking of neural connections, a mechanism that enables neurons to connect (and induce learning) or inhibit (and induce forgetting).

This is akin to optogenetics, where light-induced modification of neurons causes them to either turn on or off, enabling or inhibiting connections to the next neuron in the chain.

To develop the technology, the researchers used a material called black phosphorus (BP) that can be inherently defective in nature.

This is usually a problem for optoelectronics, but with precision engineering the researchers were able to harness the defects to create new functionality.

“Defects are usually looked on as something to be avoided, but here we’re using them to create something novel and useful,” Ahmed said.

“It’s a creative approach to finding solutions for the technical challenges we face.”

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

Multifunctional Optoelectronics via Harnessing Defects in Layered Black Phosphorus by Taimur Ahmed, Sruthi Kuriakose, Sherif Abbas,, Michelle J. S. Spencer, Md. Ataur Rahman, Muhammad Tahir, Yuerui Lu, Prashant Sonar, Vipul Bansal, Madhu Bhaskaran, Sharath Sriram, Sumeet Walia. Advanced Functional Materials DOI: https://doi.org/10.1002/adfm.201901991 First published (online): 17 July 2019

This paper is behind a paywall.

Memristors with better mimicry of synapses

It seems to me it’s been quite a while since I’ve stumbled across a memristor story from the University of Micihigan but it was worth waiting for. (Much of the research around memristors has to do with their potential application in neuromorphic (brainlike) computers.) From a December 17, 2018 news item on ScienceDaily,

A new electronic device developed at the University of Michigan can directly model the behaviors of a synapse, which is a connection between two neurons.

For the first time, the way that neurons share or compete for resources can be explored in hardware without the need for complicated circuits.

“Neuroscientists have argued that competition and cooperation behaviors among synapses are very important. Our new memristive devices allow us to implement a faithful model of these behaviors in a solid-state system,” said Wei Lu, U-M professor of electrical and computer engineering and senior author of the study in Nature Materials.

A December 17, 2018 University of Michigan news release (also on EurekAlert), which originated the news item, provides an explanation of memristors and their ‘similarity’ to synapses while providing more details about this latest research,

Memristors are electrical resistors with memory–advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. They could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning.

The memristor is a good model for a synapse. It mimics the way that the connections between neurons strengthen or weaken when signals pass through them. But the changes in conductance typically come from changes in the shape of the channels of conductive material within the memristor. These channels–and the memristor’s ability to conduct electricity–could not be precisely controlled in previous devices.

Now, the U-M team has made a memristor in which they have better command of the conducting pathways.They developed a new material out of the semiconductor molybdenum disulfide–a “two-dimensional” material that can be peeled into layers just a few atoms thick. Lu’s team injected lithium ions into the gaps between molybdenum disulfide layers.
They found that if there are enough lithium ions present, the molybdenum sulfide transforms its lattice structure, enabling electrons to run through the film easily as if it were a metal. But in areas with too few lithium ions, the molybdenum sulfide restores its original lattice structure and becomes a semiconductor, and electrical signals have a hard time getting through.

The lithium ions are easy to rearrange within the layer by sliding them with an electric field. This changes the size of the regions that conduct electricity little by little and thereby controls the device’s conductance.

“Because we change the ‘bulk’ properties of the film, the conductance change is much more gradual and much more controllable,” Lu said.

In addition to making the devices behave better, the layered structure enabled Lu’s team to link multiple memristors together through shared lithium ions–creating a kind of connection that is also found in brains. A single neuron’s dendrite, or its signal-receiving end, may have several synapses connecting it to the signaling arms of other neurons. Lu compares the availability of lithium ions to that of a protein that enables synapses to grow.

If the growth of one synapse releases these proteins, called plasticity-related proteins, other synapses nearby can also grow–this is cooperation. Neuroscientists have argued that cooperation between synapses helps to rapidly form vivid memories that last for decades and create associative memories, like a scent that reminds you of your grandmother’s house, for example. If the protein is scarce, one synapse will grow at the expense of the other–and this competition pares down our brains’ connections and keeps them from exploding with signals.
Lu’s team was able to show these phenomena directly using their memristor devices. In the competition scenario, lithium ions were drained away from one side of the device. The side with the lithium ions increased its conductance, emulating the growth, and the conductance of the device with little lithium was stunted.

In a cooperation scenario, they made a memristor network with four devices that can exchange lithium ions, and then siphoned some lithium ions from one device out to the others. In this case, not only could the lithium donor increase its conductance–the other three devices could too, although their signals weren’t as strong.

Lu’s team is currently building networks of memristors like these to explore their potential for neuromorphic computing, which mimics the circuitry of the brain.

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

Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing by Xiaojian Zhu, Da Li, Xiaogan Liang, & Wei D. Lu. Nature Materials (2018) DOI: https://doi.org/10.1038/s41563-018-0248-5 Published 17 December 2018

This paper is behind a paywall.

The researchers have made images illustrating their work available,

A schematic of the molybdenum disulfide layers with lithium ions between them. On the right, the simplified inset shows how the molybdenum disulfide changes its atom arrangements in the presence and absence of the lithium atoms, between a metal (1T’ phase) and semiconductor (2H phase), respectively. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.

A diagram of a synapse receiving a signal from one of the connecting neurons. This signal activates the generation of plasticity-related proteins (PRPs), which help a synapse to grow. They can migrate to other synapses, which enables multiple synapses to grow at once. The new device is the first to mimic this process directly, without the need for software or complicated circuits. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.
An electron microscope image showing the rectangular gold (Au) electrodes representing signalling neurons and the rounded electrode representing the receiving neuron. The material of molybdenum disulfide layered with lithium connects the electrodes, enabling the simulation of cooperative growth among synapses. Image credit: Xiaojian Zhu, Nanoelectronics Group, University of Michigan.

That’s all folks.

An artificial synapse tuned by light, a ferromagnetic memristor, and a transparent, flexible artificial synapse

Down the memristor rabbit hole one more time.* I started out with news about two new papers and inadvertently found two more. In a bid to keep this posting to a manageable size, I’m stopping at four.

UK

In a June 19, 2019 Nanowerk Spotlight article, Dr. Neil Kemp discusses memristors and some of his latest work (Note: A link has been removed),

Memristor (or memory resistors) devices are non-volatile electronic memory devices that were first theorized by Leon Chua in the 1970’s. However, it was some thirty years later that the first practical device was fabricated. This was in 2008 when a group led by Stanley Williams at HP Research Labs realized that switching of the resistance between a conducting and less conducting state in metal-oxide thin-film devices was showing Leon Chua’s memristor behaviour.

The high interest in memristor devices also stems from the fact that these devices emulate the memory and learning properties of biological synapses. i.e. the electrical resistance value of the device is dependent on the history of the current flowing through it.

There is a huge effort underway to use memristor devices in neuromorphic computing applications and it is now reasonable to imagine the development of a new generation of artificial intelligent devices with very low power consumption (non-volatile), ultra-fast performance and high-density integration.

These discoveries come at an important juncture in microelectronics, since there is increasing disparity between computational needs of Big Data, Artificial Intelligence (A.I.) and the Internet of Things (IoT), and the capabilities of existing computers. The increases in speed, efficiency and performance of computer technology cannot continue in the same manner as it has done since the 1960s.

To date, most memristor research has focussed on the electronic switching properties of the device. However, for many applications it is useful to have an additional handle (or degree of freedom) on the device to control its resistive state. For example memory and processing in the brain also involves numerous chemical and bio-chemical reactions that control the brain structure and its evolution through development.

To emulate this in a simple solid-state system composed of just switches alone is not possible. In our research, we are interested in using light to mediate this essential control.

We have demonstrated that light can be used to make short and long-term memory and we have shown how light can modulate a special type of learning, called spike timing dependent plasticity (STDP). STDP involves two neuronal spikes incident across a synapse at the same time. Depending on the relative timing of the spikes and their overlap across the synaptic cleft, the connection strength is other strengthened or weakened.

In our earlier work, we were only able to achieve to small switching effects in memristors using light. In our latest work (Advanced Electronic Materials, “Percolation Threshold Enables Optical Resistive-Memory Switching and Light-Tuneable Synaptic Learning in Segregated Nanocomposites”), we take advantage of a percolating-like nanoparticle morphology to vastly increase the magnitude of the switching between electronic resistance states when light is incident on the device.

We have used an inhomogeneous percolating network consisting of metallic nanoparticles distributed in filamentary-like conduction paths. Electronic conduction and the resistance of the device is very sensitive to any disruption of the conduction path(s).

By embedding the nanoparticles in a polymer that can expand or contract with light the conduction pathways are broken or re-connected causing very large changes in the electrical resistance and memristance of the device.

Our devices could lead to the development of new memristor-based artificial intelligence systems that are adaptive and reconfigurable using a combination of optical and electronic signalling. Furthermore, they have the potential for the development of very fast optical cameras for artificial intelligence recognition systems.

Our work provides a nice proof-of-concept but the materials used means the optical switching is slow. The materials are also not well suited to industry fabrication. In our on-going work we are addressing these switching speed issues whilst also focussing on industry compatible materials.

Currently we are working on a new type of optical memristor device that should give us orders of magnitude improvement in the optical switching speeds whilst also retaining a large difference between the resistance on and off states. We hope to be able to achieve nanosecond switching speeds. The materials used are also compatible with industry standard methods of fabrication.

The new devices should also have applications in optical communications, interfacing and photonic computing. We are currently looking for commercial investors to help fund the research on these devices so that we can bring the device specifications to a level of commercial interest.

If you’re interested in memristors, Kemp’s article is well written and quite informative for nonexperts, assuming of course you can tolerate not understanding everything perfectly.

Here are links and citations for two papers. The first is the latest referred to in the article, a May 2019 paper and the second is a paper appearing in July 2019.

Percolation Threshold Enables Optical Resistive‐Memory Switching and Light‐Tuneable Synaptic Learning in Segregated Nanocomposites by Ayoub H. Jaafar, Mary O’Neill, Stephen M. Kelly, Emanuele Verrelli, Neil T. Kemp. Advanced Electronic Materials DOI: https://doi.org/10.1002/aelm.201900197 First published: 28 May 2019

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices by Ayoub H.Jaafar, N.T.Kemp. DOI: https://doi.org/10.1016/j.carbon.2019.07.007 Carbon Available online 3 July 2019

The first paper (May 2019) is definitely behind a paywall and the second paper (July 2019) appears to be behind a paywall.

Dr. Kemp’s work has been featured here previously in a January 3, 2018 posting in the subsection titled, Shining a light on the memristor.

China

This work from China was announced in a June 20, 2019 news item on Nanowerk,

Memristors, demonstrated by solid-state devices with continuously tunable resistance, have emerged as a new paradigm for self-adaptive networks that require synapse-like functions. Spin-based memristors offer advantages over other types of memristors because of their significant endurance and high energy effciency.

However, it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Ta/CoFeB/MgO heterostructures are commonly used in interfacial PMA-based [perpendicular magnetic anisotropy] magnetic tunnel junctions, which exhibit large tunnel magnetoresistance and are implemented in commercial MRAM [magnetic random access memory] products.

“To achieve the memristive function, DW is driven back and forth in a continuous manner in the CoFeB layer by applying in-plane positive or negative current pulses along the Ta layer, utilizing SOT that the current exerts on the CoFeB magnetization,” said Shuai Zhang, a coauthor in the paper. “Slowly propagating domain wall generates a creep in the detection area of the device, which yields a broad range of intermediate resistive states in the AHE [anomalous Hall effect] measurements. Consequently, AHE resistance is modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number.”

“For a follow-up study, we are working on more neuromorphic operations, such as spike-timing-dependent plasticity and paired pulsed facilitation,” concludes You. …

Here’s are links to and citations for the paper (Note: It’s a little confusing but I believe that one of the links will take you to the online version, as for the ‘open access’ link, keep reading),

A Spin–Orbit‐Torque Memristive Device by Shuai Zhang, Shijiang Luo, Nuo Xu, Qiming Zou, Min Song, Jijun Yun, Qiang Luo, Zhe Guo, Ruofan Li, Weicheng Tian, Xin Li, Hengan Zhou, Huiming Chen, Yue Zhang, Xiaofei Yang, Wanjun Jiang, Ka Shen, Jeongmin Hong, Zhe Yuan, Li Xi, Ke Xia, Sayeef Salahuddin, Bernard Dieny, Long You. Advanced Electronic Materials Volume 5, Issue 4 April 2019 (print version) 1800782 DOI: https://doi.org/10.1002/aelm.201800782 First published [online]: 30 January 2019 Note: there is another DOI, https://doi.org/10.1002/aelm.201970022 where you can have open access to Memristors: A Spin–Orbit‐Torque Memristive Device (Adv. Electron. Mater. 4/2019)

The paper published online in January 2019 is behind a paywall and the paper (almost the same title) published in April 2019 has a new DOI and is open access. Final note: I tried accessing the ‘free’ paper and opened up a free file for the artwork featuring the work from China on the back cover of the April 2019 of Advanced Electronic Materials.

Korea

Usually when I see the words transparency and flexibility, I expect to see graphene is one of the materials. That’s not the case for this paper (link to and citation for),

Transparent and flexible photonic artificial synapse with piezo-phototronic modulator: Versatile memory capability and higher order learning algorithm by Mohit Kumar, Joondong Kim, Ching-Ping Wong. Nano Energy Volume 63, September 2019, 103843 DOI: https://doi.org/10.1016/j.nanoen.2019.06.039 Available online 22 June 2019

Here’s the abstract for the paper where you’ll see that the material is made up of zinc oxide silver nanowires,

An artificial photonic synapse having tunable manifold synaptic response can be an essential step forward for the advancement of novel neuromorphic computing. In this work, we reported the development of highly transparent and flexible two-terminal ZnO/Ag-nanowires/PET photonic artificial synapse [emphasis mine]. The device shows purely photo-triggered all essential synaptic functions such as transition from short-to long-term plasticity, paired-pulse facilitation, and spike-timing-dependent plasticity, including in the versatile memory capability. Importantly, strain-induced piezo-phototronic effect within ZnO provides an additional degree of regulation to modulate all of the synaptic functions in multi-levels. The observed effect is quantitatively explained as a dynamic of photo-induced electron-hole trapping/detraining via the defect states such as oxygen vacancies. We revealed that the synaptic functions can be consolidated and converted by applied strain, which is not previously applied any of the reported synaptic devices. This study will open a new avenue to the scientific community to control and design highly transparent wearable neuromorphic computing.

This paper is behind a paywall.

Artificial synapse courtesy of nanowires

It looks like a popsicle to me,

Caption: Image captured by an electron microscope of a single nanowire memristor (highlighted in colour to distinguish it from other nanowires in the background image). Blue: silver electrode, orange: nanowire, yellow: platinum electrode. Blue bubbles are dispersed over the nanowire. They are made up of silver ions and form a bridge between the electrodes which increases the resistance. Credit: Forschungszentrum Jülich

Not a popsicle but a representation of a device (memristor) scientists claim mimics a biological nerve cell according to a December 5, 2018 news item on ScienceDaily,

Scientists from Jülich [Germany] together with colleagues from Aachen [Germany] and Turin [Italy] have produced a memristive element made from nanowires that functions in much the same way as a biological nerve cell. The component is able to both save and process information, as well as receive numerous signals in parallel. The resistive switching cell made from oxide crystal nanowires is thus proving to be the ideal candidate for use in building bioinspired “neuromorphic” processors, able to take over the diverse functions of biological synapses and neurons.

A Dec. 5, 2018 Forschungszentrum Jülich press release (also on EurekAlert), which originated the news item, provides more details,

Computers have learned a lot in recent years. Thanks to rapid progress in artificial intelligence they are now able to drive cars, translate texts, defeat world champions at chess, and much more besides. In doing so, one of the greatest challenges lies in the attempt to artificially reproduce the signal processing in the human brain. In neural networks, data are stored and processed to a high degree in parallel. Traditional computers on the other hand rapidly work through tasks in succession and clearly distinguish between the storing and processing of information. As a rule, neural networks can only be simulated in a very cumbersome and inefficient way using conventional hardware.

Systems with neuromorphic chips that imitate the way the human brain works offer significant advantages. Experts in the field describe this type of bioinspired computer as being able to work in a decentralised way, having at its disposal a multitude of processors, which, like neurons in the brain, are connected to each other by networks. If a processor breaks down, another can take over its function. What is more, just like in the brain, where practice leads to improved signal transfer, a bioinspired processor should have the capacity to learn.

“With today’s semiconductor technology, these functions are to some extent already achievable. These systems are however suitable for particular applications and require a lot of space and energy,” says Dr. Ilia Valov from Forschungszentrum Jülich. “Our nanowire devices made from zinc oxide crystals can inherently process and even store information, as well as being extremely small and energy efficient,” explains the researcher from Jülich’s Peter Grünberg Institute.

For years memristive cells have been ascribed the best chances of being capable of taking over the function of neurons and synapses in bioinspired computers. They alter their electrical resistance depending on the intensity and direction of the electric current flowing through them. In contrast to conventional transistors, their last resistance value remains intact even when the electric current is switched off. Memristors are thus fundamentally capable of learning.

In order to create these properties, scientists at Forschungszentrum Jülich and RWTH Aachen University used a single zinc oxide nanowire, produced by their colleagues from the polytechnic university in Turin. Measuring approximately one ten-thousandth of a millimeter in size, this type of nanowire is over a thousand times thinner than a human hair. The resulting memristive component not only takes up a tiny amount of space, but also is able to switch much faster than flash memory.

Nanowires offer promising novel physical properties compared to other solids and are used among other things in the development of new types of solar cells, sensors, batteries and computer chips. Their manufacture is comparatively simple. Nanowires result from the evaporation deposition of specified materials onto a suitable substrate, where they practically grow of their own accord.

In order to create a functioning cell, both ends of the nanowire must be attached to suitable metals, in this case platinum and silver. The metals function as electrodes, and in addition, release ions triggered by an appropriate electric current. The metal ions are able to spread over the surface of the wire and build a bridge to alter its conductivity.

Components made from single nanowires are, however, still too isolated to be of practical use in chips. Consequently, the next step being planned by the Jülich and Turin researchers is to produce and study a memristive element, composed of a larger, relatively easy to generate group of several hundred nanowires offering more exciting functionalities.

The Italians have also written about the work in a December 4, 2018 news item for the Polytecnico di Torino’s inhouse magazine, PoliFlash’. I like the image they’ve used better as it offers a bit more detail and looks less like a popsicle. First, the image,

Courtesy: Polytecnico di Torino

Now, the news item, which includes some historical information about the memristor (Note: There is some repetition and links have been removed),

Emulating and understanding the human brain is one of the most important challenges for modern technology: on the one hand, the ability to artificially reproduce the processing of brain signals is one of the cornerstones for the development of artificial intelligence, while on the other the understanding of the cognitive processes at the base of the human mind is still far away.

And the research published in the prestigious journal Nature Communications by Gianluca Milano and Carlo Ricciardi, PhD student and professor, respectively, of the Applied Science and Technology Department of the Politecnico di Torino, represents a step forward in these directions. In fact, the study entitled “Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities” shows how it is possible to artificially emulate the activity of synapses, i.e. the connections between neurons that regulate the learning processes in our brain, in a single “nanowire” with a diameter thousands of times smaller than that of a hair.

It is a crystalline nanowire that takes the “memristor”, the electronic device able to artificially reproduce the functions of biological synapses, to a more performing level. Thanks to the use of nanotechnologies, which allow the manipulation of matter at the atomic level, it was for the first time possible to combine into one single device the synaptic functions that were individually emulated through specific devices. For this reason, the nanowire allows an extreme miniaturisation of the “memristor”, significantly reducing the complexity and energy consumption of the electronic circuits necessary for the implementation of learning algorithms.

Starting from the theorisation of the “memristor” in 1971 by Prof. Leon Chua – now visiting professor at the Politecnico di Torino, who was conferred an honorary degree by the University in 2015 – this new technology will not only allow smaller and more performing devices to be created for the implementation of increasingly “intelligent” computers, but is also a significant step forward for the emulation and understanding of the functioning of the brain.

“The nanowire memristor – said Carlo Ricciardirepresents a model system for the study of physical and electrochemical phenomena that govern biological synapses at the nanoscale. The work is the result of the collaboration between our research team and the RWTH University of Aachen in Germany, supported by INRiM, the National Institute of Metrological Research, and IIT, the Italian Institute of Technology.”

h.t for the Italian info. to Nanowerk’s Dec. 10, 2018 news item.

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

Self-limited single nanowire systems combining all-in-one memristive and neuromorphic functionalities by Gianluca Milano, Michael Luebben, Zheng Ma, Rafal Dunin-Borkowski, Luca Boarino, Candido F. Pirri, Rainer Waser, Carlo Ricciardi, & Ilia Valov. Nature Communicationsvolume 9, Article number: 5151 (2018) DOI: https://doi.org/10.1038/s41467-018-07330-7 Published: 04 December 2018

This paper is open access.

Just use the search term “memristor” in the blog search engine if you’re curious about the multitudinous number of postings on the topic here.

If only AI had a brain (a Wizard of Oz reference?)

The title, which I’ve borrowed from the news release, is the only Wizard of Oz reference that I can find but it works so well, you don’t really need anything more.

Moving onto the news, a July 23, 2018 news item on phys.org announces new work on developing an artificial synapse (Note: A link has been removed),

Digital computation has rendered nearly all forms of analog computation obsolete since as far back as the 1950s. However, there is one major exception that rivals the computational power of the most advanced digital devices: the human brain.

The human brain is a dense network of neurons. Each neuron is connected to tens of thousands of others, and they use synapses to fire information back and forth constantly. With each exchange, the brain modulates these connections to create efficient pathways in direct response to the surrounding environment. Digital computers live in a world of ones and zeros. They perform tasks sequentially, following each step of their algorithms in a fixed order.

A team of researchers from Pitt’s [University of Pittsburgh] Swanson School of Engineering have developed an “artificial synapse” that does not process information like a digital computer but rather mimics the analog way the human brain completes tasks. Led by Feng Xiong, assistant professor of electrical and computer engineering, the researchers published their results in the recent issue of the journal Advanced Materials (DOI: 10.1002/adma.201802353). His Pitt co-authors include Mohammad Sharbati (first author), Yanhao Du, Jorge Torres, Nolan Ardolino, and Minhee Yun.

A July 23, 2018 University of Pittsburgh Swanson School of Engineering news release (also on EurekAlert), which originated the news item, provides further information,

“The analog nature and massive parallelism of the brain are partly why humans can outperform even the most powerful computers when it comes to higher order cognitive functions such as voice recognition or pattern recognition in complex and varied data sets,” explains Dr. Xiong.

An emerging field called “neuromorphic computing” focuses on the design of computational hardware inspired by the human brain. Dr. Xiong and his team built graphene-based artificial synapses in a two-dimensional honeycomb configuration of carbon atoms. Graphene’s conductive properties allowed the researchers to finely tune its electrical conductance, which is the strength of the synaptic connection or the synaptic weight. The graphene synapse demonstrated excellent energy efficiency, just like biological synapses.

In the recent resurgence of artificial intelligence, computers can already replicate the brain in certain ways, but it takes about a dozen digital devices to mimic one analog synapse. The human brain has hundreds of trillions of synapses for transmitting information, so building a brain with digital devices is seemingly impossible, or at the very least, not scalable. Xiong Lab’s approach provides a possible route for the hardware implementation of large-scale artificial neural networks.

According to Dr. Xiong, artificial neural networks based on the current CMOS (complementary metal-oxide semiconductor) technology will always have limited functionality in terms of energy efficiency, scalability, and packing density. “It is really important we develop new device concepts for synaptic electronics that are analog in nature, energy-efficient, scalable, and suitable for large-scale integrations,” he says. “Our graphene synapse seems to check all the boxes on these requirements so far.”

With graphene’s inherent flexibility and excellent mechanical properties, these graphene-based neural networks can be employed in flexible and wearable electronics to enable computation at the “edge of the internet”–places where computing devices such as sensors make contact with the physical world.

“By empowering even a rudimentary level of intelligence in wearable electronics and sensors, we can track our health with smart sensors, provide preventive care and timely diagnostics, monitor plants growth and identify possible pest issues, and regulate and optimize the manufacturing process–significantly improving the overall productivity and quality of life in our society,” Dr. Xiong says.

The development of an artificial brain that functions like the analog human brain still requires a number of breakthroughs. Researchers need to find the right configurations to optimize these new artificial synapses. They will need to make them compatible with an array of other devices to form neural networks, and they will need to ensure that all of the artificial synapses in a large-scale neural network behave in the same exact manner. Despite the challenges, Dr. Xiong says he’s optimistic about the direction they’re headed.

“We are pretty excited about this progress since it can potentially lead to the energy-efficient, hardware implementation of neuromorphic computing, which is currently carried out in power-intensive GPU clusters. The low-power trait of our artificial synapse and its flexible nature make it a suitable candidate for any kind of A.I. device, which would revolutionize our lives, perhaps even more than the digital revolution we’ve seen over the past few decades,” Dr. Xiong says.

There is a visual representation of this artificial synapse,

Caption: Pitt engineers built a graphene-based artificial synapse in a two-dimensional, honeycomb configuration of carbon atoms that demonstrated excellent energy efficiency comparable to biological synapses Credit: Swanson School of Engineering

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

Low‐Power, Electrochemically Tunable Graphene Synapses for Neuromorphic Computing by Mohammad Taghi Sharbati, Yanhao Du, Jorge Torres, Nolan D. Ardolino, Minhee Yun, Feng Xiong. Advanced Materials DOP: https://doi.org/10.1002/adma.201802353 First published [online]: 23 July 2018

This paper is behind a paywall.

I did look at the paper and if I understand it rightly, this approach is different from the memristor-based approaches that I have so often featured here. More than that I cannot say.

Finally, the Wizard of Oz song ‘If I Only Had a Brain’,

A 3D printed eye cornea and a 3D printed copy of your brain (also: a Brad Pitt connection)

Sometimes it’s hard to keep up with 3D tissue printing news. I have two news bits, one concerning eyes and another concerning brains.

3D printed human corneas

A May 29, 2018 news item on ScienceDaily trumpets the news,

The first human corneas have been 3D printed by scientists at Newcastle University, UK.

It means the technique could be used in the future to ensure an unlimited supply of corneas.

As the outermost layer of the human eye, the cornea has an important role in focusing vision.

Yet there is a significant shortage of corneas available to transplant, with 10 million people worldwide requiring surgery to prevent corneal blindness as a result of diseases such as trachoma, an infectious eye disorder.

In addition, almost 5 million people suffer total blindness due to corneal scarring caused by burns, lacerations, abrasion or disease.

The proof-of-concept research, published today [May 29, 2018] in Experimental Eye Research, reports how stem cells (human corneal stromal cells) from a healthy donor cornea were mixed together with alginate and collagen to create a solution that could be printed, a ‘bio-ink’.

Here are the proud researchers with their cornea,

Caption: Dr. Steve Swioklo and Professor Che Connon with a dyed cornea. Credit: Newcastle University, UK

A May 30,2018 Newcastle University press release (also on EurekAlert but published on May 29, 2018), which originated the news item, adds more details,

Using a simple low-cost 3D bio-printer, the bio-ink was successfully extruded in concentric circles to form the shape of a human cornea. It took less than 10 minutes to print.

The stem cells were then shown to culture – or grow.

Che Connon, Professor of Tissue Engineering at Newcastle University, who led the work, said: “Many teams across the world have been chasing the ideal bio-ink to make this process feasible.

“Our unique gel – a combination of alginate and collagen – keeps the stem cells alive whilst producing a material which is stiff enough to hold its shape but soft enough to be squeezed out the nozzle of a 3D printer.

“This builds upon our previous work in which we kept cells alive for weeks at room temperature within a similar hydrogel. Now we have a ready to use bio-ink containing stem cells allowing users to start printing tissues without having to worry about growing the cells separately.”

The scientists, including first author and PhD student Ms Abigail Isaacson from the Institute of Genetic Medicine, Newcastle University, also demonstrated that they could build a cornea to match a patient’s unique specifications.

The dimensions of the printed tissue were originally taken from an actual cornea. By scanning a patient’s eye, they could use the data to rapidly print a cornea which matched the size and shape.

Professor Connon added: “Our 3D printed corneas will now have to undergo further testing and it will be several years before we could be in the position where we are using them for transplants.

“However, what we have shown is that it is feasible to print corneas using coordinates taken from a patient eye and that this approach has potential to combat the world-wide shortage.”

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

3D bioprinting of a corneal stroma equivalent by Abigail Isaacson, Stephen Swioklo, Che J. Connon. Experimental Eye Research Volume 173, August 2018, Pages 188–193 and 2018 May 14 pii: S0014-4835(18)30212-4. doi: 10.1016/j.exer.2018.05.010. [Epub ahead of print]

This paper is behind a paywall.

A 3D printed copy of your brain

I love the title for this May 30, 2018 Wyss Institute for Biologically Inspired Engineering news release: Creating piece of mind by Lindsay Brownell (also on EurekAlert),

What if you could hold a physical model of your own brain in your hands, accurate down to its every unique fold? That’s just a normal part of life for Steven Keating, Ph.D., who had a baseball-sized tumor removed from his brain at age 26 while he was a graduate student in the MIT Media Lab’s Mediated Matter group. Curious to see what his brain actually looked like before the tumor was removed, and with the goal of better understanding his diagnosis and treatment options, Keating collected his medical data and began 3D printing his MRI [magnetic resonance imaging] and CT [computed tomography] scans, but was frustrated that existing methods were prohibitively time-intensive, cumbersome, and failed to accurately reveal important features of interest. Keating reached out to some of his group’s collaborators, including members of the Wyss Institute at Harvard University, who were exploring a new method for 3D printing biological samples.

“It never occurred to us to use this approach for human anatomy until Steve came to us and said, ‘Guys, here’s my data, what can we do?” says Ahmed Hosny, who was a Research Fellow with at the Wyss Institute at the time and is now a machine learning engineer at the Dana-Farber Cancer Institute. The result of that impromptu collaboration – which grew to involve James Weaver, Ph.D., Senior Research Scientist at the Wyss Institute; Neri Oxman, [emphasis mine] Ph.D., Director of the MIT Media Lab’s Mediated Matter group and Associate Professor of Media Arts and Sciences; and a team of researchers and physicians at several other academic and medical centers in the US and Germany – is a new technique that allows images from MRI, CT, and other medical scans to be easily and quickly converted into physical models with unprecedented detail. The research is reported in 3D Printing and Additive Manufacturing.

“I nearly jumped out of my chair when I saw what this technology is able to do,” says Beth Ripley, M.D. Ph.D., an Assistant Professor of Radiology at the University of Washington and clinical radiologist at the Seattle VA, and co-author of the paper. “It creates exquisitely detailed 3D-printed medical models with a fraction of the manual labor currently required, making 3D printing more accessible to the medical field as a tool for research and diagnosis.”

Imaging technologies like MRI and CT scans produce high-resolution images as a series of “slices” that reveal the details of structures inside the human body, making them an invaluable resource for evaluating and diagnosing medical conditions. Most 3D printers build physical models in a layer-by-layer process, so feeding them layers of medical images to create a solid structure is an obvious synergy between the two technologies.

However, there is a problem: MRI and CT scans produce images with so much detail that the object(s) of interest need to be isolated from surrounding tissue and converted into surface meshes in order to be printed. This is achieved via either a very time-intensive process called “segmentation” where a radiologist manually traces the desired object on every single image slice (sometimes hundreds of images for a single sample), or an automatic “thresholding” process in which a computer program quickly converts areas that contain grayscale pixels into either solid black or solid white pixels, based on a shade of gray that is chosen to be the threshold between black and white. However, medical imaging data sets often contain objects that are irregularly shaped and lack clear, well-defined borders; as a result, auto-thresholding (or even manual segmentation) often over- or under-exaggerates the size of a feature of interest and washes out critical detail.

The new method described by the paper’s authors gives medical professionals the best of both worlds, offering a fast and highly accurate method for converting complex images into a format that can be easily 3D printed. The key lies in printing with dithered bitmaps, a digital file format in which each pixel of a grayscale image is converted into a series of black and white pixels, and the density of the black pixels is what defines the different shades of gray rather than the pixels themselves varying in color.

Similar to the way images in black-and-white newsprint use varying sizes of black ink dots to convey shading, the more black pixels that are present in a given area, the darker it appears. By simplifying all pixels from various shades of gray into a mixture of black or white pixels, dithered bitmaps allow a 3D printer to print complex medical images using two different materials that preserve all the subtle variations of the original data with much greater accuracy and speed.

The team of researchers used bitmap-based 3D printing to create models of Keating’s brain and tumor that faithfully preserved all of the gradations of detail present in the raw MRI data down to a resolution that is on par with what the human eye can distinguish from about 9-10 inches away. Using this same approach, they were also able to print a variable stiffness model of a human heart valve using different materials for the valve tissue versus the mineral plaques that had formed within the valve, resulting in a model that exhibited mechanical property gradients and provided new insights into the actual effects of the plaques on valve function.

“Our approach not only allows for high levels of detail to be preserved and printed into medical models, but it also saves a tremendous amount of time and money,” says Weaver, who is the corresponding author of the paper. “Manually segmenting a CT scan of a healthy human foot, with all its internal bone structure, bone marrow, tendons, muscles, soft tissue, and skin, for example, can take more than 30 hours, even by a trained professional – we were able to do it in less than an hour.”

The researchers hope that their method will help make 3D printing a more viable tool for routine exams and diagnoses, patient education, and understanding the human body. “Right now, it’s just too expensive for hospitals to employ a team of specialists to go in and hand-segment image data sets for 3D printing, except in extremely high-risk or high-profile cases. We’re hoping to change that,” says Hosny.

In order for that to happen, some entrenched elements of the medical field need to change as well. Most patients’ data are compressed to save space on hospital servers, so it’s often difficult to get the raw MRI or CT scan files needed for high-resolution 3D printing. Additionally, the team’s research was facilitated through a joint collaboration with leading 3D printer manufacturer Stratasys, which allowed access to their 3D printer’s intrinsic bitmap printing capabilities. New software packages also still need to be developed to better leverage these capabilities and make them more accessible to medical professionals.

Despite these hurdles, the researchers are confident that their achievements present a significant value to the medical community. “I imagine that sometime within the next 5 years, the day could come when any patient that goes into a doctor’s office for a routine or non-routine CT or MRI scan will be able to get a 3D-printed model of their patient-specific data within a few days,” says Weaver.

Keating, who has become a passionate advocate of efforts to enable patients to access their own medical data, still 3D prints his MRI scans to see how his skull is healing post-surgery and check on his brain to make sure his tumor isn’t coming back. “The ability to understand what’s happening inside of you, to actually hold it in your hands and see the effects of treatment, is incredibly empowering,” he says.

“Curiosity is one of the biggest drivers of innovation and change for the greater good, especially when it involves exploring questions across disciplines and institutions. The Wyss Institute is proud to be a space where this kind of cross-field innovation can flourish,” says Wyss Institute Founding Director Donald Ingber, M.D., Ph.D., who is also the Judah Folkman Professor of Vascular Biology at Harvard Medical School (HMS) and the Vascular Biology Program at Boston Children’s Hospital, as well as Professor of Bioengineering at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS).

Here’s an image illustrating the work,

Caption: This 3D-printed model of Steven Keating’s skull and brain clearly shows his brain tumor and other fine details thanks to the new data processing method pioneered by the study’s authors. Credit: Wyss Institute at Harvard University

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

From Improved Diagnostics to Presurgical Planning: High-Resolution Functionally Graded Multimaterial 3D Printing of Biomedical Tomographic Data Sets by Ahmed Hosny , Steven J. Keating, Joshua D. Dilley, Beth Ripley, Tatiana Kelil, Steve Pieper, Dominik Kolb, Christoph Bader, Anne-Marie Pobloth, Molly Griffin, Reza Nezafat, Georg Duda, Ennio A. Chiocca, James R.. Stone, James S. Michaelson, Mason N. Dean, Neri Oxman, and James C. Weaver. 3D Printing and Additive Manufacturing http://doi.org/10.1089/3dp.2017.0140 Online Ahead of Print:May 29, 2018

This paper appears to be open access.

A tangential Brad Pitt connection

It’s a bit of Hollywood gossip. There was some speculation in April 2018 that Brad Pitt was dating Dr. Neri Oxman highlighted in the Wyss Institute news release. Here’s a sample of an April 13, 2018 posting on Laineygossip (Note: A link has been removed),

It took him a long time to date, but he is now,” the insider tells PEOPLE. “He likes women who challenge him in every way, especially in the intellect department. Brad has seen how happy and different Amal has made his friend (George Clooney). It has given him something to think about.”

While a Pitt source has maintained he and Oxman are “just friends,” they’ve met up a few times since the fall and the insider notes Pitt has been flying frequently to the East Coast. He dropped by one of Oxman’s classes last fall and was spotted at MIT again a few weeks ago.

Pitt and Oxman got to know each other through an architecture project at MIT, where she works as a professor of media arts and sciences at the school’s Media Lab. Pitt has always been interested in architecture and founded the Make It Right Foundation, which builds affordable and environmentally friendly homes in New Orleans for people in need.

“One of the things Brad has said all along is that he wants to do more architecture and design work,” another source says. “He loves this, has found the furniture design and New Orleans developing work fulfilling, and knows he has a talent for it.”

It’s only been a week since Page Six first broke the news that Brad and Dr Oxman have been spending time together.

I’m fascinated by Oxman’s (and her colleagues’) furniture. Rose Brook writes about one particular Oxman piece in her March 27, 2014 posting for TCT magazine (Note: Links have been removed),

MIT Professor and 3D printing forerunner Neri Oxman has unveiled her striking acoustic chaise longue, which was made using Stratasys 3D printing technology.

Oxman collaborated with Professor W Craig Carter and Composer and fellow MIT Professor Tod Machover to explore material properties and their spatial arrangement to form the acoustic piece.

Christened Gemini, the two-part chaise was produced using a Stratasys Objet500 Connex3 multi-colour, multi-material 3D printer as well as traditional furniture-making techniques and it will be on display at the Vocal Vibrations exhibition at Le Laboratoire in Paris from March 28th 2014.

An Architect, Designer and Professor of Media, Arts and Science at MIT, Oxman’s creation aims to convey the relationship of twins in the womb through material properties and their arrangement. It was made using both subtractive and additive manufacturing and is part of Oxman’s ongoing exploration of what Stratasys’ ground-breaking multi-colour, multi-material 3D printer can do.

Brook goes on to explain how the chaise was made and the inspiration that led to it. Finally, it’s interesting to note that Oxman was working with Stratasys in 2014 and that this 2018 brain project is being developed in a joint collaboration with Statasys.

That’s it for 3D printing today.

Santiago Ramón y Cajal and the butterflies of the soul

The Cajal exhibit of drawings was here in Vancouver (Canada) this last fall (2017) and I still carry the memory of that glorious experience (see my Sept. 11, 2017 posting for more about the show and associated events). It seems Cajal’s drawings had a similar response in New York city, from a January 18, 2018 article by Roberta Smith for the New York Times,

It’s not often that you look at an exhibition with the help of the very apparatus that is its subject. But so it is with “The Beautiful Brain: The Drawings of Santiago Ramón y Cajal” at the Grey Art Gallery at New York University, one of the most unusual, ravishing exhibitions of the season.

The show finished its run on March 31, 2018 and is now on its way to the Massachusetts Institute of Technology (MIT) in Boston, Massachusetts for its opening on May 3, 2018. It looks like they have an exciting lineup of events to go along with the exhibit (from MIT’s The Beautiful Brain: The Drawings of Santiago Ramón y Cajal exhibit and event page),

SUMMER PROGRAMS

ONGOING

Spotlight Tours
Explorations led by local and Spanish scientists, artists, and entrepreneurs who will share their unique perspectives on particular aspects of the exhibition. (2:00 pm on select Tuesdays and Saturdays)

Tue, May 8 – Mark Harnett, Fred and Carole Middleton Career Development Professor at MIT and McGovern Institute Investigator Sat, May 26 – Marion Boulicault, MIT Graduate Student and Neuroethics Fellow in the Center for Sensorimotor Neural Engineering Tue, June 5 – Kelsey Allen, Graduate researcher, MIT Center for Brains, Minds, and Machines Sat, Jun 23 – Francisco Martin-Martinez, Research Scientist in MIT’s Laboratory for Atomistic & Molecular Mechanics and President of the Spanish Foundation for Science and Technology Jul 21 – Alex Gomez-Marin, Principal Investigator of the Behavior of Organisms Laboratory in the Instituto de Neurociencias, Spain Tue, Jul 31– Julie Pryor, Director of Communications at the McGovern Institute for Brain Research at MIT Tue, Aug 28 – Satrajit Ghosh, Principal Research Scientist at the McGovern Institute for Brain Research at MIT, Assistant Professor in the Department of Otolaryngology at Harvard Medical School, and faculty member in the Speech and Hearing Biosciences and Technology program in the Harvard Division of Medical Sciences

Idea Hub
Drop in and explore expansion microscopy in our maker-space.

Visualizing Science Workshop
Experiential learning with micro-scale biological images. (pre-registration required)

Gallery Demonstrations
Researchers share the latest on neural anatomy, signal transmission, and modern imaging techniques.

EVENTS

Teen Science Café: Mindful Matters
MIT researchers studying the brain share their mind-blowing findings.

Neuron Paint Night
Create a painting of cerebral cortex neurons and learn about the EyeWire citizen science game.

Cerebral Cinema Series
Hear from researchers and then compare real science to depictions on the big screen.

Brainy Trivia
Test your brain power in a night of science trivia and short, snappy research talks.

Come back to see our exciting lineup for the fall!

If you don’t have a chance to see the show or if you’d like a preview, I encourage you to read Smith’s article as it has embedded several Cajal drawings and rendered them exceptionally well.

For those who like a little contemporary (and related) science with their art, there’s a March 30, 2018 Harvard Medical Schoo (HMS)l news release by Kevin Jang (also on EurekAlert), Note: All links save one have been removed,

Drawing of the cells of the chick cerebellum by Santiago Ramón y Cajal, from “Estructura de los centros nerviosos de las aves,” Madrid, circa 1905

 

Modern neuroscience, for all its complexity, can trace its roots directly to a series of pen-and-paper sketches rendered by Nobel laureate Santiago Ramón y Cajal in the late 19th and early 20th centuries.

His observations and drawings exposed the previously hidden composition of the brain, revealing neuronal cell bodies and delicate projections that connect individual neurons together into intricate networks.

As he explored the nervous systems of various organisms under his microscope, a natural question arose: What makes a human brain different from the brain of any other species?

At least part of the answer, Ramón y Cajal hypothesized, lay in a specific class of neuron—one found in a dazzling variety of shapes and patterns of connectivity, and present in higher proportions in the human brain than in the brains of other species. He dubbed them the “butterflies of the soul.”

Known as interneurons, these cells play critical roles in transmitting information between sensory and motor neurons, and, when defective, have been linked to diseases such as schizophrenia, autism and intellectual disability.

Despite more than a century of study, however, it remains unclear why interneurons are so diverse and what specific functions the different subtypes carry out.

Now, in a study published in the March 22 [2018] issue of Nature, researchers from Harvard Medical School, New York Genome Center, New York University and the Broad Institute of MIT and Harvard have detailed for the first time how interneurons emerge and diversify in the brain.

Using single-cell analysis—a technology that allows scientists to track cellular behavior one cell at a time—the team traced the lineage of interneurons from their earliest precursor states to their mature forms in mice. The researchers identified key genetic programs that determine the fate of developing interneurons, as well as when these programs are switched on or off.

The findings serve as a guide for efforts to shed light on interneuron function and may help inform new treatment strategies for disorders involving their dysfunction, the authors said.

“We knew more than 100 years ago that this huge diversity of morphologically interesting cells existed in the brain, but their specific individual roles in brain function are still largely unclear,” said co-senior author Gordon Fishell, HMS professor of neurobiology and a faculty member at the Stanley Center for Psychiatric Research at the Broad.

“Our study provides a road map for understanding how and when distinct interneuron subtypes develop, giving us unprecedented insight into the biology of these cells,” he said. “We can now investigate interneuron properties as they emerge, unlock how these important cells function and perhaps even intervene when they fail to develop correctly in neuropsychiatric disease.”

A hippocampal interneuron. Image: Biosciences Imaging Gp, Soton, Wellcome Trust via Creative CommonsA hippocampal interneuron. Image: Biosciences Imaging Gp, Soton, Wellcome Trust via Creative Commons

Origins and Fates

In collaboration with co-senior author Rahul Satija, core faculty member of the New York Genome Center, Fishell and colleagues analyzed brain regions in developing mice known to contain precursor cells that give rise to interneurons.

Using Drop-seq, a single-cell sequencing technique created by researchers at HMS and the Broad, the team profiled gene expression in thousands of individual cells at multiple time points.

This approach overcomes a major limitation in past research, which could analyze only the average activity of mixtures of many different cells.

In the current study, the team found that the precursor state of all interneurons had similar gene expression patterns despite originating in three separate brain regions and giving rise to 14 or more interneuron subtypes alone—a number still under debate as researchers learn more about these cells.

“Mature interneuron subtypes exhibit incredible diversity. Their morphology and patterns of connectivity and activity are so different from each other, but our results show that the first steps in their maturation are remarkably similar,” said Satija, who is also an assistant professor of biology at New York University.

“They share a common developmental trajectory at the earliest stages, but the seeds of what will cause them to diverge later—a handful of genes—are present from the beginning,” Satija said.

As they profiled cells at later stages in development, the team observed the initial emergence of four interneuron “cardinal” classes, which give rise to distinct fates. Cells were committed to these fates even in the early embryo. By developing a novel computational strategy to link precursors with adult subtypes, the researchers identified individual genes that were switched on and off when cells began to diversify.

For example, they found that the gene Mef2c—mutations of which are linked to Alzheimer’s disease, schizophrenia and neurodevelopmental disorders in humans—is an early embryonic marker for a specific interneuron subtype known as Pvalb neurons. When they deleted Mef2c in animal models, Pvalb neurons failed to develop.

These early genes likely orchestrate the execution of subsequent genetic subroutines, such as ones that guide interneuron subtypes as they migrate to different locations in the brain and ones that help form unique connection patterns with other neural cell types, the authors said.

The identification of these genes and their temporal activity now provide researchers with specific targets to investigate the precise functions of interneurons, as well as how neurons diversify in general, according to the authors.

“One of the goals of this project was to address an incredibly fascinating developmental biology question, which is how individual progenitor cells decide between different neuronal fates,” Satija said. “In addition to these early markers of interneuron divergence, we found numerous additional genes that increase in expression, many dramatically, at later time points.”

The association of some of these genes with neuropsychiatric diseases promises to provide a better understanding of these disorders and the development of therapeutic strategies to treat them, a particularly important notion given the paucity of new treatments, the authors said.

Over the past 50 years, there have been no fundamentally new classes of neuropsychiatric drugs, only newer versions of old drugs, the researchers pointed out.

“Our repertoire is no better than it was in the 1970s,” Fishell said.

“Neuropsychiatric diseases likely reflect the dysfunction of very specific cell types. Our study puts forward a clear picture of what cells to look at as we work to shed light on the mechanisms that underlie these disorders,” Fishell said. “What we will find remains to be seen, but we have new, strong hypotheses that we can now test.”

As a resource for the research community, the study data and software are open-source and freely accessible online.

A gallery of the drawings of Santiago Ramón y Cajal is currently on display in New York City, and will open at the MIT Museum in Boston in May 2018.

Christian Mayer, Christoph Hafemeister and Rachel Bandler served as co-lead authors on the study.

This work was supported by the National Institutes of Health (R01 NS074972, R01 NS081297, MH071679-12, DP2-HG-009623, F30MH114462, T32GM007308, F31NS103398), the European Molecular Biology Organization, the National Science Foundation and the Simons Foundation.

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

Developmental diversification of cortical inhibitory interneurons by Christian Mayer, Christoph Hafemeister, Rachel C. Bandler, Robert Machold, Renata Batista Brito, Xavier Jaglin, Kathryn Allaway, Andrew Butler, Gord Fishell, & Rahul Satija. Nature volume 555, pages 457–462 (22 March 2018) doi:10.1038/nature25999 Published: 05 March 2018

This paper is behind a paywall.

Less is more—a superconducting synapse

It seems the US National Institute of Standards and Technology (NIST) is more deeply invested into developing artificial brains than I had realized (See: April 17, 2018 posting). A January 26, 2018 NIST news release on EurekAlert describes the organization’s latest foray into the field,

Researchers at the National Institute of Standards and Technology (NIST) have built a superconducting switch that “learns” like a biological system and could connect processors and store memories in future computers operating like the human brain.

The NIST switch, described in Science Advances, is called a synapse, like its biological counterpart, and it supplies a missing piece for so-called neuromorphic computers. Envisioned as a new type of artificial intelligence, such computers could boost perception and decision-making for applications such as self-driving cars and cancer diagnosis.

A synapse is a connection or switch between two brain cells. NIST’s artificial synapse–a squat metallic cylinder 10 micrometers in diameter–is like the real thing because it can process incoming electrical spikes to customize spiking output signals. This processing is based on a flexible internal design that can be tuned by experience or its environment. The more firing between cells or processors, the stronger the connection. Both the real and artificial synapses can thus maintain old circuits and create new ones. Even better than the real thing, the NIST synapse can fire much faster than the human brain–1 billion times per second, compared to a brain cell’s 50 times per second–using just a whiff of energy, about one ten-thousandth as much as a human synapse. In technical terms, the spiking energy is less than 1 attojoule, lower than the background energy at room temperature and on a par with the chemical energy bonding two atoms in a molecule.

“The NIST synapse has lower energy needs than the human synapse, and we don’t know of any other artificial synapse that uses less energy,” NIST physicist Mike Schneider said.

The new synapse would be used in neuromorphic computers made of superconducting components, which can transmit electricity without resistance, and therefore, would be more efficient than other designs based on semiconductors or software. Data would be transmitted, processed and stored in units of magnetic flux. Superconducting devices mimicking brain cells and transmission lines have been developed, but until now, efficient synapses–a crucial piece–have been missing.

The brain is especially powerful for tasks like context recognition because it processes data both in sequence and simultaneously and stores memories in synapses all over the system. A conventional computer processes data only in sequence and stores memory in a separate unit.

The NIST synapse is a Josephson junction, long used in NIST voltage standards. These junctions are a sandwich of superconducting materials with an insulator as a filling. When an electrical current through the junction exceeds a level called the critical current, voltage spikes are produced. The synapse uses standard niobium electrodes but has a unique filling made of nanoscale clusters of manganese in a silicon matrix.

The nanoclusters–about 20,000 per square micrometer–act like tiny bar magnets with “spins” that can be oriented either randomly or in a coordinated manner.

“These are customized Josephson junctions,” Schneider said. “We can control the number of nanoclusters pointing in the same direction, which affects the superconducting properties of the junction.”

The synapse rests in a superconducting state, except when it’s activated by incoming current and starts producing voltage spikes. Researchers apply current pulses in a magnetic field to boost the magnetic ordering, that is, the number of nanoclusters pointing in the same direction. This magnetic effect progressively reduces the critical current level, making it easier to create a normal conductor and produce voltage spikes.

The critical current is the lowest when all the nanoclusters are aligned. The process is also reversible: Pulses are applied without a magnetic field to reduce the magnetic ordering and raise the critical current. This design, in which different inputs alter the spin alignment and resulting output signals, is similar to how the brain operates.

Synapse behavior can also be tuned by changing how the device is made and its operating temperature. By making the nanoclusters smaller, researchers can reduce the pulse energy needed to raise or lower the magnetic order of the device. Raising the operating temperature slightly from minus 271.15 degrees C (minus 456.07 degrees F) to minus 269.15 degrees C (minus 452.47 degrees F), for example, results in more and higher voltage spikes.

Crucially, the synapses can be stacked in three dimensions (3-D) to make large systems that could be used for computing. NIST researchers created a circuit model to simulate how such a system would operate.

The NIST synapse’s combination of small size, superfast spiking signals, low energy needs and 3-D stacking capability could provide the means for a far more complex neuromorphic system than has been demonstrated with other technologies, according to the paper.

NIST has prepared an animation illustrating the research,

Caption: This is an animation of how NIST’s artificial synapse works. Credit: Sean Kelley/NIST

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

Ultralow power artificial synapses using nanotextured magnetic Josephson junctions by Michael L. Schneider, Christine A. Donnelly, Stephen E. Russek, Burm Baek, Matthew R. Pufall, Peter F. Hopkins, Paul D. Dresselhaus, Samuel P. Benz, and William H. Rippard. Science Advances 26 Jan 2018: Vol. 4, no. 1, e1701329 DOI: 10.1126/sciadv.1701329

This paper is open access.

Samuel K. Moore in a January 26, 2018 posting on the Nanoclast blog (on the IEEE [Institute for Electrical and Electronics Engineers] website) describes the research and adds a few technical explanations such as this about the Josephson junction,

In a magnetic Josephson junction, that “weak link” is magnetic. The higher the magnetic field, the lower the critical current needed to produce voltage spikes. In the device Schneider and his colleagues designed, the magnetic field is caused by 20,000 or so nanometer-scale clusters of manganese embedded in silicon. …

Moore also provides some additional links including this one to his November 29, 2017 posting where he describes four new approaches to computing including quantum computing and neuromorphic (brain-like) computing.