Category Archives: neuromorphic engineering

Brain composer

This is a representation of the work they are doing on brain-computer interfaces (BCI) at the Technical University of Graz (TU Graz; Austria),

A Sept. 11, 2017 news item on announces the research into thinking melodies turning them into a musical score,

TU Graz researchers develop new brain-computer interface application that allows music to be composed by the power of thought. They have published their results in the current issue of the journal PLOS ONE.

Brain-computer interfaces (BCI) can replace bodily functions to a certain degree. Thanks to BCI, physically impaired persons can control special prostheses via their minds, surf the internet and write emails.

A group led by BCI expert Gernot Müller-Putz from TU Graz’s Institute of Neural Engineering shows that experiences of quite a different tone can be sounded from the keys of brain-computer interfaces. Derived from an established BCI method for writing, the team has developed a new application by which music can be composed and transferred onto a musical score through the power of thought. It employs a special cap that measures brain waves, the adapted BCI, music composition software, and a bit of musical knowledge.

A Sept. 6, 2017 TU Graz press release by Suzanne Eigner, which originated the news item, explains the research in more detail,

The basic principle of the BCI method used, which is called P300, can be briefly described: various options, such as letters or notes, pauses, chords, etc. flash by one after the other in a table. If you’re trained and can focus on the desired option while it lights up, you cause a minute change in your brain waves. The BCI recognises this change and draws conclusions about the chosen option.

Musical test persons

18 test persons chosen for the study by Gernot Müller-Putz, Andreas Pinegger and Selina C. Wriessnegger from TU Graz’s Institute of Neural Engineering as well as Hannah Hiebel, meanwhile at the Institute of Cognitive Psychology & Neuroscience at the University of Graz, had to “think” melodies onto a musical score. All test subjects were of sound bodily health during the study and had a certain degree of basic musical and compositional knowledge since they all played musical instruments to some degree. Among the test persons was the late Graz composer and clarinettist, Franz Cibulka. “The results of the BCI compositions can really be heard. And what is more important: the test persons enjoyed it. After a short training session, all of them could start composing and seeing their melodies on the score and then play them. The very positive results of the study with bodily healthy test persons are the first step in a possible expansion of the BCI composition to patients,” stresses Müller-Putz.

Sideshow of BCI research

This little-noticed sideshow of the lively BCI research at TU Graz, with its distinct focus on disabled persons, shows us which other avenues may yet be worth exploring. Meanwhile there are some initial attempts at BCI systems on smart phones. This makes it easier for people to use BCI applications, since the smart phone as powerful computer is becoming part of the BCI system. It is thus conceivable, for instance, to have BCI apps which can analyse brain signals for various applications. “20 years ago, the idea of composing a piece of music using the power of the mind was unimaginable. Now we can do it, and at the same time have tens of new, different ideas which are in part, once again, a long way from becoming reality. We still need a bit more time before it is mature enough for daily applications. The BCI community is working in many directions at high pressure.

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

Composing only by thought: Novel application of the P300 brain-computer interface by Andreas Pinegger, Hannah Hiebel, Selina C. Wriessnegger, Gernot R. Müller-Putz. PLOS Published: September 6, 2017

This paper is open access.

This BCI ‘sideshow’ reminded me of The Music Man, a musical by Meredith Wilson. It was both a play and a film  and I’ve only ever seen the 1962 film. It features a con man, Harold Hill, who sells musical instruments and uniforms in small towns in Iowa. He has no musical training but while he’s conning the townspeople he convinces them that he can provide musical training with his ‘think method’. After falling in love with one of the townsfolk, he is hunted down and made to prove his method works. This is a clip from a Broadway revival of the play where Harold Hill is hoping that his ‘think method’ while yield results,

Of course, the people in this study had musicaltraining so they could think a melody into a musical score but I find the echo from the past amusing nonetheless.

Neuristors and brainlike computing

As you might suspect, a neuristor is based on a memristor .(For a description of a memristor there’s this Wikipedia entry and you can search this blog with the tags ‘memristor’ and neuromorphic engineering’ for more here.)

Being new to neuristors ,I needed a little more information before reading the latest and found this Dec. 24, 2012 article by John Timmer for Ars Technica (Note: Links have been removed),

Computing hardware is composed of a series of binary switches; they’re either on or off. The other piece of computational hardware we’re familiar with, the brain, doesn’t work anything like that. Rather than being on or off, individual neurons exhibit brief spikes of activity, and encode information in the pattern and timing of these spikes. The differences between the two have made it difficult to model neurons using computer hardware. In fact, the recent, successful generation of a flexible neural system required that each neuron be modeled separately in software in order to get the sort of spiking behavior real neurons display.

But researchers may have figured out a way to create a chip that spikes. The people at HP labs who have been working on memristors have figured out a combination of memristors and capacitors that can create a spiking output pattern. Although these spikes appear to be more regular than the ones produced by actual neurons, it might be possible to create versions that are a bit more variable than this one. And, more significantly, it should be possible to fabricate them in large numbers, possibly right on a silicon chip.

The key to making the devices is something called a Mott insulator. These are materials that would normally be able to conduct electricity, but are unable to because of interactions among their electrons. Critically, these interactions weaken with elevated temperatures. So, by heating a Mott insulator, it’s possible to turn it into a conductor. In the case of the material used here, NbO2, the heat is supplied by resistance itself. By applying a voltage to the NbO2 in the device, it becomes a resistor, heats up, and, when it reaches a critical temperature, turns into a conductor, allowing current to flow through. But, given the chance to cool off, the device will return to its resistive state. Formally, this behavior is described as a memristor.

To get the sort of spiking behavior seen in a neuron, the authors turned to a simplified model of neurons based on the proteins that allow them to transmit electrical signals. When a neuron fires, sodium channels open, allowing ions to rush into a nerve cell, and changing the relative charges inside and outside its membrane. In response to these changes, potassium channels then open, allowing different ions out, and restoring the charge balance. That shuts the whole thing down, and allows various pumps to start restoring the initial ion balance.

Here’s a link to and a citation for the research paper described in Timmer’s article,

A scalable neuristor built with Mott memristors by Matthew D. Pickett, Gilberto Medeiros-Ribeiro, & R. Stanley Williams. Nature Materials 12, 114–117 (2013) doi:10.1038/nmat3510 Published online 16 December 2012

This paper is behind a paywall.

A July 28, 2017 news item on Nanowerk provides an update on neuristors,

A future android brain like that of Star Trek’s Commander Data might contain neuristors, multi-circuit components that emulate the firings of human neurons.

Neuristors already exist today in labs, in small quantities, and to fuel the quest to boost neuristors’ power and numbers for practical use in brain-like computing, the U.S. Department of Defense has awarded a $7.1 million grant to a research team led by the Georgia Institute of Technology. The researchers will mainly work on new metal oxide materials that buzz electronically at the nanoscale to emulate the way human neural networks buzz with electric potential on a cellular level.

A July 28, 2017 Georgia Tech news release, which originated the news item, delves further into neuristors and the proposed work leading to an artificial retina that can learn (!). This was not where I was expecting things to go,

But let’s walk expectations back from the distant sci-fi future into the scientific present: The research team is developing its neuristor materials to build an intelligent light sensor, and not some artificial version of the human brain, which would require hundreds of trillions of circuits.

“We’re not going to reach circuit complexities of that magnitude, not even a tenth,” said Alan Doolittle, a professor at Georgia Tech’s School of Electrical and Computer Engineering. “Also, currently science doesn’t really know yet very well how the human brain works, so we can’t duplicate it.”

Intelligent retina

But an artificial retina that can learn autonomously appears well within reach of the research team from Georgia Tech and Binghamton University. Despite the term “retina,” the development is not intended as a medical implant, but it could be used in advanced image recognition cameras for national defense and police work.

At the same time, it would significantly advance brain-mimicking, or neuromorphic, computing. The research field that takes its cues from what science already does know about how the brain computes to develop exponentially more powerful computing.

The retina would be comprised of an array of ultra-compact circuits called neuristors (a word combining “neuron” and “transistor”) that sense light, compute an image out of it and store the image. All three of the functions would occur simultaneously and nearly instantaneously.

“The same device senses, computes and stores the image,” Doolittle said. “The device is the sensor, and it’s the processor, and it’s the memory all at the same time.” A neuristor itself is comprised in part of devices called memristors inspired by the way human neurons work.

Brain vs. PC

That cuts out loads of processing and memory lag time that are inherent in traditional computing.

Take the device you’re reading this article on: Its microprocessor has to tap a separate memory component to get data, then do some processing, tap memory again for more data, process some more, etc. “That back-and-forth from memory to microprocessor has created a bottleneck,” Doolittle said.

A neuristor array breaks the bottleneck by emulating the extreme flexibility of biological nervous systems: When a brain computes, it uses a broad set of neural pathways that flash with enormous data. Then, later, to compute the same thing again, it will use quite different neural paths.

Traditional computer pathways, by contrast, are hardwired. For example, look at a present-day processor and you’ll see lines etched into it. Those are pathways that computational signals are limited to.

The new memristor materials at the heart of the neuristor are not etched, and signals flow through the surface very freely, more like they do through the brain, exponentially increasing the number of possible pathways computation can take. That helps the new intelligent retina compute powerfully and swiftly.

Terrorists, missing children

The retina’s memory could also store thousands of photos, allowing it to immediately match up what it sees with the saved images. The retina could pinpoint known terror suspects in a crowd, find missing children, or identify enemy aircraft virtually instantaneously, without having to trawl databases to correctly identify what is in the images.

Even if you take away the optics, the new neuristor arrays still advance artificial intelligence. Instead of light, a surface of neuristors could absorb massive data streams at once, compute them, store them, and compare them to patterns of other data, immediately. It could even autonomously learn to extrapolate further information, like calculating the third dimension out of data from two dimensions.

“It will work with anything that has a repetitive pattern like radar signatures, for example,” Doolittle said. “Right now, that’s too challenging to compute, because radar information is flying out at such a high data rate that no computer can even think about keeping up.”

Smart materials

The research project’s title acronym CEREBRAL may hint at distant dreams of an artificial brain, but what it stands for spells out the present goal in neuromorphic computing: Cross-disciplinary Electronic-ionic Research Enabling Biologically Realistic Autonomous Learning.

The intelligent retina’s neuristors are based on novel metal oxide nanotechnology materials, unique to Georgia Tech. They allow computing signals to flow flexibly across pathways that are electronic, which is customary in computing, and at the same time make use of ion motion, which is more commonly know from the way batteries and biological systems work.

The new materials have already been created, and they work, but the researchers don’t yet fully understand why.

Much of the project is dedicated to examining quantum states in the materials and how those states help create useful electronic-ionic properties. Researchers will view them by bombarding the metal oxides with extremely bright x-ray photons at the recently constructed National Synchrotron Light Source II.

Grant sub-awardee Binghamton University is located close by, and Binghamton physicists will run experiments and hone them via theoretical modeling.

‘Sea of lithium’

The neuristors are created mainly by the way the metal oxide materials are grown in the lab, which has advantages over building neuristors in a more wired way.

This materials-growing approach is conducive to mass production. Also, though neuristors in general free signals to take multiple pathways, Georgia Tech’s neuristors do it much more flexibly thanks to chemical properties.

“We also have a sea of lithium, and it’s like an infinite reservoir of computational ionic fluid,” Doolittle said. The lithium niobite imitates the way ionic fluid bathes biological neurons and allows them to flash with electric potential while signaling. In a neuristor array, the lithium niobite helps computational signaling move in myriad directions.

“It’s not like the typical semiconductor material, where you etch a line, and only that line has the computational material,” Doolittle said.

Commander Data’s brain?

“Unlike any other previous neuristors, our neuristors will adapt themselves in their computational-electronic pulsing on the fly, which makes them more like a neurological system,” Doolittle said. “They mimic biology in that we have ion drift across the material to create the memristors (the memory part of neuristors).”

Brains are far superior to computers at most things, but not all. Brains recognize objects and do motor tasks much better. But computers are much better at arithmetic and data processing.

Neuristor arrays can meld both types of computing, making them biological and algorithmic at once, a bit like Commander Data’s brain.

The research is being funded through the U.S. Department of Defense’s Multidisciplinary University Research Initiatives (MURI) Program under grant number FOA: N00014-16-R-FO05. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of those agencies.

Fascinating, non?

Self-learning neuromorphic chip

There aren’t many details about this chip and so far as I can tell this technology is not based on a memristor. From a May 16, 2017 news item on,

Today [May 16, 2017], at the imec technology forum (ITF2017), imec demonstrated the world’s first self-learning neuromorphic chip. The brain-inspired chip, based on OxRAM technology, has the capability of self-learning and has been demonstrated to have the ability to compose music.

Here’s a sample,

A May 16, 2017 imec press release, which originated the news item, expands on the theme,

The human brain is a dream for computer scientists: it has a huge computing power while consuming only a few tens of Watts. Imec researchers are combining state-of-the-art hardware and software to design chips that feature these desirable characteristics of a self-learning system. Imec’s ultimate goal is to design the process technology and building blocks to make artificial intelligence to be energy efficient so that that it can be integrated into sensors. Such intelligent sensors will drive the internet of things forward. This would not only allow machine learning to be present in all sensors but also allow on-field learning capability to further improve the learning.

By co-optimizing the hardware and the software, the chip features machine learning and intelligence characteristics on a small area, while consuming only very little power. The chip is self-learning, meaning that is makes associations between what it has experienced and what it experiences. The more it experiences, the stronger the connections will be. The chip presented today has learned to compose new music and the rules for the composition are learnt on the fly.

It is imec’s ultimate goal to further advance both hardware and software to achieve very low-power, high-performance, low-cost and highly miniaturized neuromorphic chips that can be applied in many domains ranging for personal health, energy, traffic management etc. For example, neuromorphic chips integrated into sensors for health monitoring would enable to identify a particular heartrate change that could lead to heart abnormalities, and would learn to recognize slightly different ECG patterns that vary between individuals. Such neuromorphic chips would thus enable more customized and patient-centric monitoring.

“Because we have hardware, system design and software expertise under one roof, imec is ideally positioned to drive neuromorphic computing forward,” says Praveen Raghavan, distinguished member of the technical Staff at imec. “Our chip has evolved from co-optimizing logic, memory, algorithms and system in a holistic way. This way, we succeeded in developing the building blocks for such a self-learning system.”

About ITF

The Imec Technology Forum (ITF) is imec’s series of internationally acclaimed events with a clear focus on the technologies that will drive groundbreaking innovation in healthcare, smart cities and mobility, ICT, logistics and manufacturing, and energy.

At ITF, some of the world’s greatest minds in technology take the stage. Their talks cover a wide range of domains – such as advanced chip scaling, smart imaging, sensor and communication systems, the IoT, supercomputing, sustainable energy and battery technology, and much more. As leading innovators in their fields, they also present early insights in market trends, evolutions, and breakthroughs in nanoelectronics and digital technology: What will be successful and what not, in five or even ten years from now? How will technology evolve, and how fast? And who can help you implement your technology roadmaps?

About imec

Imec is the world-leading research and innovation hub in nano-electronics and digital technologies. The combination of our widely-acclaimed leadership in microchip technology and profound software and ICT expertise is what makes us unique. By leveraging our world-class infrastructure and local and global ecosystem of partners across a multitude of industries, we create groundbreaking innovation in application domains such as healthcare, smart cities and mobility, logistics and manufacturing, and energy.

As a trusted partner for companies, start-ups and universities we bring together close to 3,500 brilliant minds from over 75 nationalities. Imec is headquartered in Leuven, Belgium and also has distributed R&D groups at a number of Flemish universities, in the Netherlands, Taiwan, USA, China, and offices in India and Japan. In 2016, imec’s revenue (P&L) totaled 496 million euro. Further information on imec can be found at

Imec is a registered trademark for the activities of IMEC International (a legal entity set up under Belgian law as a “stichting van openbaar nut”), imec Belgium (IMEC vzw supported by the Flemish Government), imec the Netherlands (Stichting IMEC Nederland, part of Holst Centre which is supported by the Dutch Government), imec Taiwan (IMEC Taiwan Co.) and imec China (IMEC Microelectronics (Shanghai) Co. Ltd.) and imec India (Imec India Private Limited), imec Florida (IMEC USA nanoelectronics design center).

I don’t usually include the ‘abouts’ but I was quite intrigued by imec. For anyone curious about the ITF (imec Forums), here’s a website with a listing all of the previously held and upcoming 2017 forums.

Emerging technology and the law

I have three news bits about legal issues that are arising as a consequence of emerging technologies.

Deep neural networks, art, and copyright

Caption: The rise of automated art opens new creative avenues, coupled with new problems for copyright protection. Credit: Provided by: Alexander Mordvintsev, Christopher Olah and Mike Tyka

Presumably this artwork is a demonstration of automated art although they never really do explain how in the news item/news release. An April 26, 2017 news item on ScienceDaily announces research into copyright and the latest in using neural networks to create art,

In 1968, sociologist Jean Baudrillard wrote on automatism that “contained within it is the dream of a dominated world […] that serves an inert and dreamy humanity.”

With the growing popularity of Deep Neural Networks (DNN’s), this dream is fast becoming a reality.

Dr. Jean-Marc Deltorn, researcher at the Centre d’études internationales de la propriété intellectuelle in Strasbourg, argues that we must remain a responsive and responsible force in this process of automation — not inert dominators. As he demonstrates in a recent Frontiers in Digital Humanities paper, the dream of automation demands a careful study of the legal problems linked to copyright.

An April 26, 2017 Frontiers (publishing) news release on EurekAlert, which originated the news item, describes the research in more detail,

For more than half a century, artists have looked to computational processes as a way of expanding their vision. DNN’s are the culmination of this cross-pollination: by learning to identify a complex number of patterns, they can generate new creations.

These systems are made up of complex algorithms modeled on the transmission of signals between neurons in the brain.

DNN creations rely in equal measure on human inputs and the non-human algorithmic networks that process them.

Inputs are fed into the system, which is layered. Each layer provides an opportunity for a more refined knowledge of the inputs (shape, color, lines). Neural networks compare actual outputs to expected ones, and correct the predictive error through repetition and optimization. They train their own pattern recognition, thereby optimizing their learning curve and producing increasingly accurate outputs.

The deeper the layers are, the higher the level of abstraction. The highest layers are able to identify the contents of a given input with reasonable accuracy, after extended periods of training.

Creation thus becomes increasingly automated through what Deltorn calls “the arcane traceries of deep architecture”. The results are sufficiently abstracted from their sources to produce original creations that have been exhibited in galleries, sold at auction and performed at concerts.

The originality of DNN’s is a combined product of technological automation on one hand, human inputs and decisions on the other.

DNN’s are gaining popularity. Various platforms (such as DeepDream) now allow internet users to generate their very own new creations . This popularization of the automation process calls for a comprehensive legal framework that ensures a creator’s economic and moral rights with regards to his work – copyright protection.

Form, originality and attribution are the three requirements for copyright. And while DNN creations satisfy the first of these three, the claim to originality and attribution will depend largely on a given country legislation and on the traceability of the human creator.

Legislation usually sets a low threshold to originality. As DNN creations could in theory be able to create an endless number of riffs on source materials, the uncurbed creation of original works could inflate the existing number of copyright protections.

Additionally, a small number of national copyright laws confers attribution to what UK legislation defines loosely as “the person by whom the arrangements necessary for the creation of the work are undertaken.” In the case of DNN’s, this could mean anybody from the programmer to the user of a DNN interface.

Combined with an overly supple take on originality, this view on attribution would further increase the number of copyrightable works.

The risk, in both cases, is that artists will be less willing to publish their own works, for fear of infringement of DNN copyright protections.

In order to promote creativity – one seminal aim of copyright protection – the issue must be limited to creations that manifest a personal voice “and not just the electric glint of a computational engine,” to quote Deltorn. A delicate act of discernment.

DNN’s promise new avenues of creative expression for artists – with potential caveats. Copyright protection – a “catalyst to creativity” – must be contained. Many of us gently bask in the glow of an increasingly automated form of technology. But if we want to safeguard the ineffable quality that defines much art, it might be a good idea to hone in more closely on the differences between the electric and the creative spark.

This research is and be will part of a broader Frontiers Research Topic collection of articles on Deep Learning and Digital Humanities.

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

Deep Creations: Intellectual Property and the Automata by Jean-Marc Deltorn. Front. Digit. Humanit., 01 February 2017 |

This paper is open access.

Conference on governance of emerging technologies

I received an April 17, 2017 notice via email about this upcoming conference. Here’s more from the Fifth Annual Conference on Governance of Emerging Technologies: Law, Policy and Ethics webpage,

The Fifth Annual Conference on Governance of Emerging Technologies:

Law, Policy and Ethics held at the new

Beus Center for Law & Society in Phoenix, AZ

May 17-19, 2017!

Call for Abstracts – Now Closed

The conference will consist of plenary and session presentations and discussions on regulatory, governance, legal, policy, social and ethical aspects of emerging technologies, including (but not limited to) nanotechnology, synthetic biology, gene editing, biotechnology, genomics, personalized medicine, human enhancement technologies, telecommunications, information technologies, surveillance technologies, geoengineering, neuroscience, artificial intelligence, and robotics. The conference is premised on the belief that there is much to be learned and shared from and across the governance experience and proposals for these various emerging technologies.

Keynote Speakers:

Gillian HadfieldRichard L. and Antoinette Schamoi Kirtland Professor of Law and Professor of Economics USC [University of Southern California] Gould School of Law

Shobita Parthasarathy, Associate Professor of Public Policy and Women’s Studies, Director, Science, Technology, and Public Policy Program University of Michigan

Stuart Russell, Professor at [University of California] Berkeley, is a computer scientist known for his contributions to artificial intelligence

Craig Shank, Vice President for Corporate Standards Group in Microsoft’s Corporate, External and Legal Affairs (CELA)

Plenary Panels:

Innovation – Responsible and/or Permissionless

Ellen-Marie Forsberg, Senior Researcher/Research Manager at Oslo and Akershus University College of Applied Sciences

Adam Thierer, Senior Research Fellow with the Technology Policy Program at the Mercatus Center at George Mason University

Wendell Wallach, Consultant, ethicist, and scholar at Yale University’s Interdisciplinary Center for Bioethics

 Gene Drives, Trade and International Regulations

Greg Kaebnick, Director, Editorial Department; Editor, Hastings Center Report; Research Scholar, Hastings Center

Jennifer Kuzma, Goodnight-North Carolina GlaxoSmithKline Foundation Distinguished Professor in Social Sciences in the School of Public and International Affairs (SPIA) and co-director of the Genetic Engineering and Society (GES) Center at North Carolina State University

Andrew Maynard, Senior Sustainability Scholar, Julie Ann Wrigley Global Institute of Sustainability Director, Risk Innovation Lab, School for the Future of Innovation in Society Professor, School for the Future of Innovation in Society, Arizona State University

Gary Marchant, Regents’ Professor of Law, Professor of Law Faculty Director and Faculty Fellow, Center for Law, Science & Innovation, Arizona State University

Marc Saner, Inaugural Director of the Institute for Science, Society and Policy, and Associate Professor, University of Ottawa Department of Geography

Big Data

Anupam Chander, Martin Luther King, Jr. Professor of Law and Director, California International Law Center, UC Davis School of Law

Pilar Ossorio, Professor of Law and Bioethics, University of Wisconsin, School of Law and School of Medicine and Public Health; Morgridge Institute for Research, Ethics Scholar-in-Residence

George Poste, Chief Scientist, Complex Adaptive Systems Initiative (CASI) (, Regents’ Professor and Del E. Webb Chair in Health Innovation, Arizona State University

Emily Shuckburgh, climate scientist and deputy head of the Polar Oceans Team at the British Antarctic Survey, University of Cambridge

 Responsible Development of AI

Spring Berman, Ira A. Fulton Schools of Engineering, Arizona State University

John Havens, The IEEE [Institute of Electrical and Electronics Engineers] Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems

Subbarao Kambhampati, Senior Sustainability Scientist, Julie Ann Wrigley Global Institute of Sustainability, Professor, School of Computing, Informatics and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University

Wendell Wallach, Consultant, Ethicist, and Scholar at Yale University’s Interdisciplinary Center for Bioethics

Existential and Catastrophic Ricks [sic]

Tony Barrett, Co-Founder and Director of Research of the Global Catastrophic Risk Institute

Haydn Belfield,  Academic Project Administrator, Centre for the Study of Existential Risk at the University of Cambridge

Margaret E. Kosal Associate Director, Sam Nunn School of International Affairs, Georgia Institute of Technology

Catherine Rhodes,  Academic Project Manager, Centre for the Study of Existential Risk at CSER, University of Cambridge

These were the panels that are of interest to me; there are others on the homepage.

Here’s some information from the Conference registration webpage,

Early Bird Registration – $50 off until May 1! Enter discount code: earlybirdGETs50

New: Group Discount – Register 2+ attendees together and receive an additional 20% off for all group members!

Click Here to Register!

Conference registration fees are as follows:

  • General (non-CLE) Registration: $150.00
  • CLE Registration: $350.00
  • *Current Student / ASU Law Alumni Registration: $50.00
  • ^Cybsersecurity sessions only (May 19): $100 CLE / $50 General / Free for students (registration info coming soon)

There you have it.

Neuro-techno future laws

I’m pretty sure this isn’t the first exploration of potential legal issues arising from research into neuroscience although it’s the first one I’ve stumbled across. From an April 25, 2017 news item on,

New human rights laws to prepare for advances in neurotechnology that put the ‘freedom of the mind’ at risk have been proposed today in the open access journal Life Sciences, Society and Policy.

The authors of the study suggest four new human rights laws could emerge in the near future to protect against exploitation and loss of privacy. The four laws are: the right to cognitive liberty, the right to mental privacy, the right to mental integrity and the right to psychological continuity.

An April 25, 2017 Biomed Central news release on EurekAlert, which originated the news item, describes the work in more detail,

Marcello Ienca, lead author and PhD student at the Institute for Biomedical Ethics at the University of Basel, said: “The mind is considered to be the last refuge of personal freedom and self-determination, but advances in neural engineering, brain imaging and neurotechnology put the freedom of the mind at risk. Our proposed laws would give people the right to refuse coercive and invasive neurotechnology, protect the privacy of data collected by neurotechnology, and protect the physical and psychological aspects of the mind from damage by the misuse of neurotechnology.”

Advances in neurotechnology, such as sophisticated brain imaging and the development of brain-computer interfaces, have led to these technologies moving away from a clinical setting and into the consumer domain. While these advances may be beneficial for individuals and society, there is a risk that the technology could be misused and create unprecedented threats to personal freedom.

Professor Roberto Andorno, co-author of the research, explained: “Brain imaging technology has already reached a point where there is discussion over its legitimacy in criminal court, for example as a tool for assessing criminal responsibility or even the risk of reoffending. Consumer companies are using brain imaging for ‘neuromarketing’, to understand consumer behaviour and elicit desired responses from customers. There are also tools such as ‘brain decoders’ which can turn brain imaging data into images, text or sound. All of these could pose a threat to personal freedom which we sought to address with the development of four new human rights laws.”

The authors explain that as neurotechnology improves and becomes commonplace, there is a risk that the technology could be hacked, allowing a third-party to ‘eavesdrop’ on someone’s mind. In the future, a brain-computer interface used to control consumer technology could put the user at risk of physical and psychological damage caused by a third-party attack on the technology. There are also ethical and legal concerns over the protection of data generated by these devices that need to be considered.

International human rights laws make no specific mention to neuroscience, although advances in biomedicine have become intertwined with laws, such as those concerning human genetic data. Similar to the historical trajectory of the genetic revolution, the authors state that the on-going neurorevolution will force a reconceptualization of human rights laws and even the creation of new ones.

Marcello Ienca added: “Science-fiction can teach us a lot about the potential threat of technology. Neurotechnology featured in famous stories has in some cases already become a reality, while others are inching ever closer, or exist as military and commercial prototypes. We need to be prepared to deal with the impact these technologies will have on our personal freedom.”

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

Towards new human rights in the age of neuroscience and neurotechnology by Marcello Ienca and Roberto Andorno. Life Sciences, Society and Policy201713:5 DOI: 10.1186/s40504-017-0050-1 Published: 26 April 2017

©  The Author(s). 2017

This paper is open access.

Spintronics-based artificial intelligence

Courtesy: Tohoku University

Japanese researchers have managed to mimic a synapse (artificial neural network) with a spintronics-based device according to a Dec. 19, 2016 Tohoku University press release (also on EurekAlert but dated Dec. 20, 2016),

Researchers at Tohoku University have, for the first time, successfully demonstrated the basic operation of spintronics-based artificial intelligence.

Artificial intelligence, which emulates the information processing function of the brain that can quickly execute complex and complicated tasks such as image recognition and weather prediction, has attracted growing attention and has already been partly put to practical use.

The currently-used artificial intelligence works on the conventional framework of semiconductor-based integrated circuit technology. However, this lacks the compactness and low-power feature of the human brain. To overcome this challenge, the implementation of a single solid-state device that plays the role of a synapse is highly promising.

The Tohoku University research group of Professor Hideo Ohno, Professor Shigeo Sato, Professor Yoshihiko Horio, Associate Professor Shunsuke Fukami and Assistant Professor Hisanao Akima developed an artificial neural network in which their recently-developed spintronic devices, comprising micro-scale magnetic material, are employed (Fig. 1). The used spintronic device is capable of memorizing arbitral values between 0 and 1 in an analogue manner unlike the conventional magnetic devices, and thus perform the learning function, which is served by synapses in the brain.

Using the developed network (Fig. 2), the researchers examined an associative memory operation, which is not readily executed by conventional computers. Through the multiple trials, they confirmed that the spintronic devices have a learning ability with which the developed artificial neural network can successfully associate memorized patterns (Fig. 3) from their input noisy versions just like the human brain can.

The proof-of-concept demonstration in this research is expected to open new horizons in artificial intelligence technology – one which is of a compact size, and which simultaneously achieves fast-processing capabilities and ultralow-power consumption. These features should enable the artificial intelligence to be used in a broad range of societal applications such as image/voice recognition, wearable terminals, sensor networks and nursing-care robots.

Here are Fig. 1 and Fig. 2, as mentioned in the press release,

Fig. 1. (a) Optical photograph of a fabricated spintronic device that serves as artificial synapse in the present demonstration. Measurement circuit for the resistance switching is also shown. (b) Measured relation between the resistance of the device and applied current, showing analogue-like resistance variation. (c) Photograph of spintronic device array mounted on a ceramic package, which is used for the developed artificial neural network. Courtesy: Tohoku University

Fig. 2. Block diagram of developed artificial neural network, consisting of PC, FPGA, and array of spintronics (spin-orbit torque; SOT) devices. Courtesy: Tohoku University

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

Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation by William A. Borders, Hisanao Akima1, Shunsuke Fukami, Satoshi Moriya, Shouta Kurihara, Yoshihiko Horio, Shigeo Sato, and Hideo Ohno. Applied Physics Express, Volume 10, Number 1 Published 20 December 2016

© 2017 The Japan Society of Applied Physics

This is an open access paper.

For anyone interested in my other posts on memristors, artificial brains, and artificial intelligence, you can search this blog for those terms  and/or Neuromorphic Engineering in the Categories section.

A bionic hybrid neurochip from the University of Calgary (Canada)

The University of Calgary is publishing some very exciting work these days as can be seen in my Sept. 21, 2016 posting about quantum teleportation. Today, the university announced this via an Oct. 26, 2016 news item on Nanowerk (Note: A link has been removed),

Brain functions are controlled by millions of brain cells. However, in order to understand how the brain controls functions, such as simple reflexes or learning and memory, we must be able to record the activity of large networks and groups of neurons. Conventional methods have allowed scientists to record the activity of neurons for minutes, but a new technology, developed by University of Calgary researchers, known as a bionic hybrid neuro chip, is able to record activity in animal brain cells for weeks at a much higher resolution. The technological advancement was published in the journal Scientific Reports(“A novel bio-mimicking, planar nano-edge microelectrode enables enhanced long-term neural recording”).

There’s more from an Oct. 26, 2016 University of Calgary news release on EurekAlert, which originated the news item,

“These chips are 15 times more sensitive than conventional neuro chips,” says Naweed Syed, PhD, scientific director of the University of Calgary, Cumming School of Medicine’s Alberta Children’s Hospital Research Institute, member of the Hotchkiss Brain Institute and senior author on the study. “This allows brain cell signals to be amplified more easily and to see real time recordings of brain cell activity at a resolution that has never been achieved before.”

The development of this technology will allow researchers to investigate and understand in greater depth, in animal models, the origins of neurological diseases and conditions such as epilepsy, as well as other cognitive functions such as learning and memory.

“Recording this activity over a long period of time allows you to see changes that occur over time, in the activity itself,” says Pierre Wijdenes, a PhD student in the Biomedical Engineering Graduate Program and the study’s first author. “This helps to understand why certain neurons form connections with each other and why others won’t.”

The cross-faculty team created the chip to mimic the natural biological contact between brain cells, essentially tricking the brain cells into believing that they are connecting with other brain cells. As a result, the cells immediately connect with the chip, thereby allowing researchers to view and record the two-way communication that would go on between two normal functioning brain cells.

“We simulated what mother-nature does in nature and provided brain cells with an environment where they feel as if they are at home,” says Syed. “This has allowed us to increase the sensitivity of our readings and help neurons build a long-term relationship with our electronic chip.”

While the chip is currently used to analyze animal brain cells, this increased resolution and the ability to make long-term recordings is bringing the technology one step closer to being effective in the recording of human brain cell activity.

“Human brain cell signals are smaller and therefore require more sensitive electronic tools to be designed to pick up the signals,” says Colin Dalton, Adjunct Professor in the Department of Electrical and Computer Engineering at the Schulich School of Engineering and a co-author on this study. Dalton is also the Facility Manager of the University of Calgary’s Advanced Micro/nanosystems Integration Facility (AMIF), where the chips were designed and fabricated.

Researchers hope the technology will one day be used as a tool to bring personalized therapeutic options to patients facing neurological disease.

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

A novel bio-mimicking, planar nano-edge microelectrode enables enhanced long-term neural recording by Pierre Wijdenes, Hasan Ali, Ryden Armstrong, Wali Zaidi, Colin Dalton & Naweed I. Syed. Scientific Reports 6, Article number: 34553 (2016) doi:10.1038/srep34553
Published online: 12 October 2016

This paper is  open access.

Are they just computer games or are we in a race with technology?

This story poses some interesting questions that touch on the uneasiness being felt as computers get ‘smarter’. From an April 13, 2016 news item on ScienceDaily,

The saying of philosopher René Descartes of what makes humans unique is beginning to sound hollow. ‘I think — therefore soon I am obsolete’ seems more appropriate. When a computer routinely beats us at chess and we can barely navigate without the help of a GPS, have we outlived our place in the world? Not quite. Welcome to the front line of research in cognitive skills, quantum computers and gaming.

Today there is an on-going battle between man and machine. While genuine machine consciousness is still years into the future, we are beginning to see computers make choices that previously demanded a human’s input. Recently, the world held its breath as Google’s algorithm AlphaGo beat a professional player in the game Go–an achievement demonstrating the explosive speed of development in machine capabilities.

An April 13, 2016 Aarhus University press release (also on EurekAlert) by Rasmus Rørbæk, which originated the news item, further develops the point,

But we are not beaten yet — human skills are still superior in some areas. This is one of the conclusions of a recent study by Danish physicist Jacob Sherson, published in the journal Nature.

“It may sound dramatic, but we are currently in a race with technology — and steadily being overtaken in many areas. Features that used to be uniquely human are fully captured by contemporary algorithms. Our results are here to demonstrate that there is still a difference between the abilities of a man and a machine,” explains Jacob Sherson.

At the interface between quantum physics and computer games, Sherson and his research group at Aarhus University have identified one of the abilities that still makes us unique compared to a computer’s enormous processing power: our skill in approaching problems heuristically and solving them intuitively. The discovery was made at the AU Ideas Centre CODER, where an interdisciplinary team of researchers work to transfer some human traits to the way computer algorithms work. ?

Quantum physics holds the promise of immense technological advances in areas ranging from computing to high-precision measurements. However, the problems that need to be solved to get there are so complex that even the most powerful supercomputers struggle with them. This is where the core idea behind CODER–combining the processing power of computers with human ingenuity — becomes clear. ?

Our common intuition

Like Columbus in QuantumLand, the CODER research group mapped out how the human brain is able to make decisions based on intuition and accumulated experience. This is done using the online game “Quantum Moves.” Over 10,000 people have played the game that allows everyone contribute to basic research in quantum physics.

“The map we created gives us insight into the strategies formed by the human brain. We behave intuitively when we need to solve an unknown problem, whereas for a computer this is incomprehensible. A computer churns through enormous amounts of information, but we can choose not to do this by basing our decision on experience or intuition. It is these intuitive insights that we discovered by analysing the Quantum Moves player solutions,” explains Jacob Sherson. ? [sic]

The laws of quantum physics dictate an upper speed limit for data manipulation, which in turn sets the ultimate limit to the processing power of quantum computers — the Quantum Speed ??Limit. Until now a computer algorithm has been used to identify this limit. It turns out that with human input researchers can find much better solutions than the algorithm.

“The players solve a very complex problem by creating simple strategies. Where a computer goes through all available options, players automatically search for a solution that intuitively feels right. Through our analysis we found that there are common features in the players’ solutions, providing a glimpse into the shared intuition of humanity. If we can teach computers to recognise these good solutions, calculations will be much faster. In a sense we are downloading our common intuition to the computer” says Jacob Sherson.

And it works. The group has shown that we can break the Quantum Speed Limit by combining the cerebral cortex and computer chips. This is the new powerful tool in the development of quantum computers and other quantum technologies.

After the buildup, the press release focuses on citizen science and computer games,

Science is often perceived as something distant and exclusive, conducted behind closed doors. To enter you have to go through years of education, and preferably have a doctorate or two. Now a completely different reality is materialising.? [sic]

In recent years, a new phenomenon has appeared–citizen science breaks down the walls of the laboratory and invites in everyone who wants to contribute. The team at Aarhus University uses games to engage people in voluntary science research. Every week people around the world spend 3 billion hours playing games. Games are entering almost all areas of our daily life and have the potential to become an invaluable resource for science.

“Who needs a supercomputer if we can access even a small fraction of this computing power? By turning science into games, anyone can do research in quantum physics. We have shown that games break down the barriers between quantum physicists and people of all backgrounds, providing phenomenal insights into state-of-the-art research. Our project combines the best of both worlds and helps challenge established paradigms in computational research,” explains Jacob Sherson.

The difference between the machine and us, figuratively speaking, is that we intuitively reach for the needle in a haystack without knowing exactly where it is. We ‘guess’ based on experience and thereby skip a whole series of bad options. For Quantum Moves, intuitive human actions have been shown to be compatible with the best computer solutions. In the future it will be exciting to explore many other problems with the aid of human intuition.

“We are at the borderline of what we as humans can understand when faced with the problems of quantum physics. With the problem underlying Quantum Moves we give the computer every chance to beat us. Yet, over and over again we see that players are more efficient than machines at solving the problem. While Hollywood blockbusters on artificial intelligence are starting to seem increasingly realistic, our results demonstrate that the comparison between man and machine still sometimes favours us. We are very far from computers with human-type cognition,” says Jacob Sherson and continues:

“Our work is first and foremost a big step towards the understanding of quantum physical challenges. We do not know if this can be transferred to other challenging problems, but it is definitely something that we will work hard to resolve in the coming years.”

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

Exploring the quantum speed limit with computer games by Jens Jakob W. H. Sørensen, Mads Kock Pedersen, Michael Munch, Pinja Haikka, Jesper Halkjær Jensen, Tilo Planke, Morten Ginnerup Andreasen, Miroslav Gajdacz, Klaus Mølmer, Andreas Lieberoth, & Jacob F. Sherson. Nature 532, 210–213  (14 April 2016) doi:10.1038/nature17620 Published online 13 April 2016

This paper is behind a paywall.

3D microtopographic scaffolds for transplantation and generation of reprogrammed human neurons

Should this technology prove successful once they start testing on people, the stated goal is to use it for the treatment of human neurodegenerative disorders such as Parkinson’s disease.  But, I can’t help wondering if they might also consider constructing an artificial brain.

Getting back to the 3D scaffolds for neurons, a March 17, 2016 US National Institutes of Health (NIH) news release (also on EurekAlert), makes the announcement,

National Institutes of Health-funded scientists have developed a 3D micro-scaffold technology that promotes reprogramming of stem cells into neurons, and supports growth of neuronal connections capable of transmitting electrical signals. The injection of these networks of functioning human neural cells — compared to injecting individual cells — dramatically improved their survival following transplantation into mouse brains. This is a promising new platform that could make transplantation of neurons a viable treatment for a broad range of human neurodegenerative disorders.

Previously, transplantation of neurons to treat neurodegenerative disorders, such as Parkinson’s disease, had very limited success due to poor survival of neurons that were injected as a solution of individual cells. The new research is supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of NIH.

“Working together, the stem cell biologists and the biomaterials experts developed a system capable of shuttling neural cells through the demanding journey of transplantation and engraftment into host brain tissue,” said Rosemarie Hunziker, Ph.D., director of the NIBIB Program in Tissue Engineering and Regenerative Medicine. “This exciting work was made possible by the close collaboration of experts in a wide range of disciplines.”

The research was performed by researchers from Rutgers University, Piscataway, New Jersey, departments of Biomedical Engineering, Neuroscience and Cell Biology, Chemical and Biochemical Engineering, and the Child Health Institute; Stanford University School of Medicine’s Institute of Stem Cell Biology and Regenerative Medicine, Stanford, California; the Human Genetics Institute of New Jersey, Piscataway; and the New Jersey Center for Biomaterials, Piscataway. The results are reported in the March 17, 2016 issue of Nature Communications.

The researchers experimented in creating scaffolds made of different types of polymer fibers, and of varying thickness and density. They ultimately created a web of relatively thick fibers using a polymer that stem cells successfully adhered to. The stem cells used were human induced pluripotent stem cells (iPSCs), which can be readily generated from adult cell types such as skin cells. The iPSCs were induced to differentiate into neural cells by introducing the protein NeuroD1 into the cells.

The space between the polymer fibers turned out to be critical. “If the scaffolds were too dense, the stem cell-derived neurons were unable to integrate into the scaffold, whereas if they are too sparse then the network organization tends to be poor,” explained Prabhas Moghe, Ph.D., distinguished professor of biomedical engineering & chemical engineering at Rutgers University and co-senior author of the paper. “The optimal pore size was one that was large enough for the cells to populate the scaffold but small enough that the differentiating neurons sensed the presence of their neighbors and produced outgrowths resulting in cell-to-cell contact. This contact enhances cell survival and development into functional neurons able to transmit an electrical signal across the developing neural network.”

To test the viability of neuron-seeded scaffolds when transplanted, the researchers created micro-scaffolds that were small enough for injection into mouse brain tissue using a standard hypodermic needle. They injected scaffolds carrying the human neurons into brain slices from mice and compared them to human neurons injected as individual, dissociated cells.

The neurons on the scaffolds had dramatically increased cell-survival compared with the individual cell suspensions. The scaffolds also promoted improved neuronal outgrowth and electrical activity. Neurons injected individually in suspension resulted in very few cells surviving the transplant procedure.

Human neurons on scaffolds compared to neurons in solution were then tested when injected into the brains of live mice. Similar to the results in the brain slices, the survival rate of neurons on the scaffold network was increased nearly 40-fold compared to injected isolated cells. A critical finding was that the neurons on the micro-scaffolds expressed proteins that are involved in the growth and maturation of neural synapses–a good indication that the transplanted neurons were capable of functionally integrating into the host brain tissue.

The success of the study gives this interdisciplinary group reason to believe that their combined areas of expertise have resulted in a system with much promise for eventual treatment of human neurodegenerative disorders. In fact, they are now refining their system for specific use as an eventual transplant therapy for Parkinson’s disease. The plan is to develop methods to differentiate the stem cells into neurons that produce dopamine, the specific neuron type that degenerates in individuals with Parkinson’s disease. The work also will include fine-tuning the scaffold materials, mechanics and dimensions to optimize the survival and function of dopamine-producing neurons, and finding the best mouse models of the disease to test this Parkinson’s-specific therapy.

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

Generation and transplantation of reprogrammed human neurons in the brain using 3D microtopographic scaffolds by Aaron L. Carlson, Neal K. Bennett, Nicola L. Francis, Apoorva Halikere, Stephen Clarke, Jennifer C. Moore, Ronald P. Hart, Kenneth Paradiso, Marius Wernig, Joachim Kohn, Zhiping P. Pang, & Prabhas V. Moghe. Nature Communications 7, Article number: 10862  doi:10.1038/ncomms10862 Published 17 March 2016

This paper is open access.

US White House’s grand computing challenge could mean a boost for research into artificial intelligence and brains

An Oct. 20, 2015 posting by Lynn Bergeson on Nanotechnology Now announces a US White House challenge incorporating nanotechnology, computing, and brain research (Note: A link has been removed),

On October 20, 2015, the White House announced a grand challenge to develop transformational computing capabilities by combining innovations in multiple scientific disciplines. See The Office of Science and Technology Policy (OSTP) states that, after considering over 100 responses to its June 17, 2015, request for information, it “is excited to announce the following grand challenge that addresses three Administration priorities — the National Nanotechnology Initiative, the National Strategic Computing Initiative (NSCI), and the BRAIN initiative.” The grand challenge is to “[c]reate a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.”

Here’s where the Oct. 20, 2015 posting, which originated the news item, by Lloyd Whitman, Randy Bryant, and Tom Kalil for the US White House blog gets interesting,

 While it continues to be a national priority to advance conventional digital computing—which has been the engine of the information technology revolution—current technology falls far short of the human brain in terms of both the brain’s sensing and problem-solving abilities and its low power consumption. Many experts predict that fundamental physical limitations will prevent transistor technology from ever matching these twin characteristics. We are therefore challenging the nanotechnology and computer science communities to look beyond the decades-old approach to computing based on the Von Neumann architecture as implemented with transistor-based processors, and chart a new path that will continue the rapid pace of innovation beyond the next decade.

There are growing problems facing the Nation that the new computing capabilities envisioned in this challenge might address, from delivering individualized treatments for disease, to allowing advanced robots to work safely alongside people, to proactively identifying and blocking cyber intrusions. To meet this challenge, major breakthroughs are needed not only in the basic devices that store and process information and the amount of energy they require, but in the way a computer analyzes images, sounds, and patterns; interprets and learns from data; and identifies and solves problems. [emphases mine]

Many of these breakthroughs will require new kinds of nanoscale devices and materials integrated into three-dimensional systems and may take a decade or more to achieve. These nanotechnology innovations will have to be developed in close coordination with new computer architectures, and will likely be informed by our growing understanding of the brain—a remarkable, fault-tolerant system that consumes less power than an incandescent light bulb.

Recent progress in developing novel, low-power methods of sensing and computation—including neuromorphic, magneto-electronic, and analog systems—combined with dramatic advances in neuroscience and cognitive sciences, lead us to believe that this ambitious challenge is now within our reach. …

This is the first time I’ve come across anything that publicly links the BRAIN initiative to computing, artificial intelligence, and artificial brains. (For my own sake, I make an arbitrary distinction between algorithms [artificial intelligence] and devices that simulate neural plasticity [artificial brains].)The emphasis in the past has always been on new strategies for dealing with Parkinson’s and other neurological diseases and conditions.

TrueNorth, a brain-inspired chip architecture from IBM and Cornell University

As a Canadian, “true north” is invariably followed by “strong and free” while singing our national anthem. For many Canadians it is almost the only phrase that is remembered without hesitation.  Consequently, some of the buzz surrounding the publication of a paper celebrating ‘TrueNorth’, a brain-inspired chip, is a bit disconcerting. Nonetheless, here is the latest IBM (in collaboration with Cornell University) news from an Aug. 8, 2014 news item on Nanowerk,

Scientists from IBM unveiled the first neurosynaptic computer chip to achieve an unprecedented scale of one million programmable neurons, 256 million programmable synapses and 46 billion synaptic operations per second per watt. At 5.4 billion transistors, this fully functional and production-scale chip is currently one of the largest CMOS chips ever built, yet, while running at biological real time, it consumes a minuscule 70mW—orders of magnitude less power than a modern microprocessor. A neurosynaptic supercomputer the size of a postage stamp that runs on the energy equivalent of a hearing-aid battery, this technology could transform science, technology, business, government, and society by enabling vision, audition, and multi-sensory applications.

An Aug. 7, 2014 IBM news release, which originated the news item, provides an overview of the multi-year process this breakthrough represents (Note: Links have been removed),

There is a huge disparity between the human brain’s cognitive capability and ultra-low power consumption when compared to today’s computers. To bridge the divide, IBM scientists created something that didn’t previously exist—an entirely new neuroscience-inspired scalable and efficient computer architecture that breaks path with the prevailing von Neumann architecture used almost universally since 1946.

This second generation chip is the culmination of almost a decade of research and development, including the initial single core hardware prototype in 2011 and software ecosystem with a new programming language and chip simulator in 2013.

The new cognitive chip architecture has an on-chip two-dimensional mesh network of 4096 digital, distributed neurosynaptic cores, where each core module integrates memory, computation, and communication, and operates in an event-driven, parallel, and fault-tolerant fashion. To enable system scaling beyond single-chip boundaries, adjacent chips, when tiled, can seamlessly connect to each other—building a foundation for future neurosynaptic supercomputers. To demonstrate scalability, IBM also revealed a 16-chip system with sixteen million programmable neurons and four billion programmable synapses.

“IBM has broken new ground in the field of brain-inspired computers, in terms of a radically new architecture, unprecedented scale, unparalleled power/area/speed efficiency, boundless scalability, and innovative design techniques. We foresee new generations of information technology systems – that complement today’s von Neumann machines – powered by an evolving ecosystem of systems, software, and services,” said Dr. Dharmendra S. Modha, IBM Fellow and IBM Chief Scientist, Brain-Inspired Computing, IBM Research. “These brain-inspired chips could transform mobility, via sensory and intelligent applications that can fit in the palm of your hand but without the need for Wi-Fi. This achievement underscores IBM’s leadership role at pivotal transformational moments in the history of computing via long-term investment in organic innovation.”

The Defense Advanced Research Projects Agency (DARPA) has funded the project since 2008 with approximately $53M via Phase 0, Phase 1, Phase 2, and Phase 3 of the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program. Current collaborators include Cornell Tech and iniLabs, Ltd.

Building the Chip

The chip was fabricated using Samsung’s 28nm process technology that has a dense on-chip memory and low-leakage transistors.

“It is an astonishing achievement to leverage a process traditionally used for commercially available, low-power mobile devices to deliver a chip that emulates the human brain by processing extreme amounts of sensory information with very little power,” said Shawn Han, vice president of Foundry Marketing, Samsung Electronics. “This is a huge architectural breakthrough that is essential as the industry moves toward the next-generation cloud and big-data processing. It’s a pleasure to be part of technical progress for next-generation through Samsung’s 28nm technology.”

The event-driven circuit elements of the chip used the asynchronous design methodology developed at Cornell Tech [aka Cornell University] and refined with IBM since 2008.

“After years of collaboration with IBM, we are now a step closer to building a computer similar to our brain,” said Professor Rajit Manohar, Cornell Tech.

The combination of cutting-edge process technology, hybrid asynchronous-synchronous design methodology, and new architecture has led to a power density of 20mW/cm2 which is nearly four orders of magnitude less than today’s microprocessors.

Advancing the SyNAPSE Ecosystem

The new chip is a component of a complete end-to-end vertically integrated ecosystem spanning a chip simulator, neuroscience data, supercomputing, neuron specification, programming paradigm, algorithms and applications, and prototype design models. The ecosystem supports all aspects of the programming cycle from design through development, debugging, and deployment.

To bring forth this fundamentally different technological capability to society, IBM has designed a novel teaching curriculum for universities, customers, partners, and IBM employees.

Applications and Vision

This ecosystem signals a shift in moving computation closer to the data, taking in vastly varied kinds of sensory data, analyzing and integrating real-time information in a context-dependent way, and dealing with the ambiguity found in complex, real-world environments.

Looking to the future, IBM is working on integrating multi-sensory neurosynaptic processing into mobile devices constrained by power, volume and speed; integrating novel event-driven sensors with the chip; real-time multimedia cloud services accelerated by neurosynaptic systems; and neurosynaptic supercomputers by tiling multiple chips on a board, creating systems that would eventually scale to one hundred trillion synapses and beyond.

Building on previously demonstrated neurosynaptic cores with on-chip, online learning, IBM envisions building learning systems that adapt in real world settings. While today’s hardware is fabricated using a modern CMOS process, the underlying architecture is poised to exploit advances in future memory, 3D integration, logic, and sensor technologies to deliver even lower power, denser package, and faster speed.

I have two articles that may prove of interest, Peter Stratton’s Aug. 7, 2014 article for The Conversation provides an easy-to-read introduction to both brains, human and computer, (as they apply to this research) and TrueNorth (h/t also hosts Stratton’s article). There’s also an Aug. 7, 2014 article by Rob Farber for which includes information from a range of text and video sources about TrueNorth and cognitive computing as it’s also known (well worth checking out).

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

A million spiking-neuron integrated circuit with a scalable communication network and interface by Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, Andrew S. Cassidy, Jun Sawada, Filipp Akopyan, Bryan L. Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, Bernard Brezzo, Ivan Vo, Steven K. Esser, Rathinakumar Appuswamy, Brian Taba, Arnon Amir, Myron D. Flickner, William P. Risk, Rajit Manohar, and Dharmendra S. Modha. Science 8 August 2014: Vol. 345 no. 6197 pp. 668-673 DOI: 10.1126/science.1254642

This paper is behind a paywall.