Category Archives: neuromorphic engineering

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 https://doi.org/10.7567/APEX.10.013007. 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 https://www.whitehouse.gov/blog/2015/10/15/nanotechnology-inspired-grand-challenge-future-computing 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 phys.org also hosts Stratton’s article). There’s also an Aug. 7, 2014 article by Rob Farber for techenablement.com 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.

US military wants you to remember

While this July 10, 2014 news item on ScienceDaily concerns DARPA, an implantable neural device, and the Lawrence Livermore National Laboratory (LLNL), it is a new project and not the one featured here in a June 18, 2014 posting titled: ‘DARPA (US Defense Advanced Research Projects Agency) awards funds for implantable neural interface’.

The new project as per the July 10, 2014 news item on ScienceDaily concerns memory,

The Department of Defense’s Defense Advanced Research Projects Agency (DARPA) awarded Lawrence Livermore National Laboratory (LLNL) up to $2.5 million to develop an implantable neural device with the ability to record and stimulate neurons within the brain to help restore memory, DARPA officials announced this week.

The research builds on the understanding that memory is a process in which neurons in certain regions of the brain encode information, store it and retrieve it. Certain types of illnesses and injuries, including Traumatic Brain Injury (TBI), Alzheimer’s disease and epilepsy, disrupt this process and cause memory loss. TBI, in particular, has affected 270,000 military service members since 2000.

A July 2, 2014 LLNL news release, which originated the news item, provides more detail,

The goal of LLNL’s work — driven by LLNL’s Neural Technology group and undertaken in collaboration with the University of California, Los Angeles (UCLA) and Medtronic — is to develop a device that uses real-time recording and closed-loop stimulation of neural tissues to bridge gaps in the injured brain and restore individuals’ ability to form new memories and access previously formed ones.

Specifically, the Neural Technology group will seek to develop a neuromodulation system — a sophisticated electronics system to modulate neurons — that will investigate areas of the brain associated with memory to understand how new memories are formed. The device will be developed at LLNL’s Center for Bioengineering.

“Currently, there is no effective treatment for memory loss resulting from conditions like TBI,” said LLNL’s project leader Satinderpall Pannu, director of the LLNL’s Center for Bioengineering, a unique facility dedicated to fabricating biocompatible neural interfaces. …

LLNL will develop a miniature, wireless and chronically implantable neural device that will incorporate both single neuron and local field potential recordings into a closed-loop system to implant into TBI patients’ brains. The device — implanted into the entorhinal cortex and hippocampus — will allow for stimulation and recording from 64 channels located on a pair of high-density electrode arrays. The entorhinal cortex and hippocampus are regions of the brain associated with memory.

The arrays will connect to an implantable electronics package capable of wireless data and power telemetry. An external electronic system worn around the ear will store digital information associated with memory storage and retrieval and provide power telemetry to the implantable package using a custom RF-coil system.

Designed to last throughout the duration of treatment, the device’s electrodes will be integrated with electronics using advanced LLNL integration and 3D packaging technologies. The microelectrodes that are the heart of this device are embedded in a biocompatible, flexible polymer.

Using the Center for Bioengineering’s capabilities, Pannu and his team of engineers have achieved 25 patents and many publications during the last decade. The team’s goal is to build the new prototype device for clinical testing by 2017.

Lawrence Livermore’s collaborators, UCLA and Medtronic, will focus on conducting clinical trials and fabricating parts and components, respectively.

“The RAM [Restoring Active Memory] program poses a formidable challenge reaching across multiple disciplines from basic brain research to medicine, computing and engineering,” said Itzhak Fried, lead investigator for the UCLA on this project and  professor of neurosurgery and psychiatry and biobehavioral sciences at the David Geffen School of Medicine at UCLA and the Semel Institute for Neuroscience and Human Behavior. “But at the end of the day, it is the suffering individual, whether an injured member of the armed forces or a patient with Alzheimer’s disease, who is at the center of our thoughts and efforts.”

LLNL’s work on the Restoring Active Memory program supports [US] President [Barack] Obama’s Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative.

Obama’s BRAIN is picking up speed.

Wacky oxide. biological synchronicity, and human brainlike computing

Research out of Pennsylvania State University (Penn State, US) has uncovered another approach  to creating artificial brains (more about the other approaches later in this post), from a May 14, 2014 news item on Science Daily,

Current computing is based on binary logic — zeroes and ones — also called Boolean computing. A new type of computing architecture that stores information in the frequencies and phases of periodic signals could work more like the human brain to do computing using a fraction of the energy of today’s computers.

A May 14, 2014 Pennsylvania State University news release, which originated the news item, describes the research in more detail,

Vanadium dioxide (VO2) is called a “wacky oxide” because it transitions from a conducting metal to an insulating semiconductor and vice versa with the addition of a small amount of heat or electrical current. A device created by electrical engineers at Penn State uses a thin film of VO2 on a titanium dioxide substrate to create an oscillating switch. Using a standard electrical engineering trick, Nikhil Shukla, a Ph.D. student in the group of Professor Suman Datta and co-advised by Professor Roman Engel-Herbert at Penn State, added a series resistor to the oxide device to stabilize their oscillations over billions of cycles. When Shukla added a second similar oscillating system, he discovered that over time the two devices would begin to oscillate in unison. This coupled system could provide the basis for non-Boolean computing. The results are reported in the May 14 [2014] online issue of Nature Publishing Group’s Scientific Reports.

“It’s called a small-world network,” explained Shukla. “You see it in lots of biological systems, such as certain species of fireflies. The males will flash randomly, but then for some unknown reason the flashes synchronize over time.” The brain is also a small-world network of closely clustered nodes that evolved for more efficient information processing.

“Biological synchronization is everywhere,” added Datta, professor of electrical engineering at Penn State and formerly a Principal Engineer in the Advanced Transistor and Nanotechnology Group at Intel Corporation. “We wanted to use it for a different kind of computing called associative processing, which is an analog rather than digital way to compute.” An array of oscillators can store patterns, for instance, the color of someone’s hair, their height and skin texture. If a second area of oscillators has the same pattern, they will begin to synchronize, and the degree of match can be read out. “They are doing this sort of thing already digitally, but it consumes tons of energy and lots of transistors,” Datta said. Datta is collaborating with co-author and Professor of Computer Science and Engineering, Vijay Narayanan, in exploring the use of these coupled oscillations in solving visual recognition problems more efficiently than existing embedded vision processors as part of a National Science Foundation Expedition in Computing program.

Shukla and Datta called on the expertise of Cornell University materials scientist Darrell Schlom to make the VO2 thin film, which has extremely high quality similar to single crystal silicon. Georgia Tech computer engineer Arijit Raychowdhury and graduate student Abhinav Parihar mathematically simulated the nonlinear dynamics of coupled phase transitions in the VO2 devices. Parihar created a short video* simulation of the transitions, which occur at a rate close to a million times per second, to show the way the oscillations synchronize. Penn State professor of materials science and engineering Venkatraman Gopalan used the Advanced Photon Source at Argonne National laboratory to visually characterize the structural changes occurring in the oxide thin film in the midst of the oscillations.

Datta believes it will take seven to ten years to scale up from their current network of two-three coupled oscillators to the 100 million or so closely packed oscillators required to make a neuromorphic computer chip. One of the benefits of the novel device is that it will use only about one percent of the energy of digital computing, allowing for new ways to design computers. Much work remains to determine if VO2 can be integrated into current silicon wafer technology. “It’s a fundamental building block for a different computing paradigm that is analog rather than digital,” Shukla concluded.

There are two papers being published about this work,

Synchronizing a single-electron shuttle to an external drive by Michael J Moeckel, Darren R Southworth, Eva M Weig, and Florian Marquardt. New J. Phys. 16 043009 doi:10.1088/1367-2630/16/4/043009

Synchronized charge oscillations in correlated electron systems by Nikhil Shukla, Abhinav Parihar, Eugene Freeman, Hanjong Paik, Greg Stone, Vijaykrishnan Narayanan, Haidan Wen, Zhonghou Cai, Venkatraman Gopalan, Roman Engel-Herbert, Darrell G. Schlom, Arijit Raychowdhury & Suman Datta. Scientific Reports 4, Article number: 4964 doi:10.1038/srep04964 Published 14 May 2014

Both articles are open access.

Finally, the researchers have provided a video animation illustrating their vanadium dioxide switches in action,

As noted earlier, there are other approaches to creating an artificial brain, i.e., neuromorphic engineering. My April 7, 2014 posting is the most recent synopsis posted here; it includes excerpts from a Nanowerk Spotlight article overview along with a mention of the ‘brain jelly’ approach and a discussion of my somewhat extensive coverage of memristors and a mention of work on nanoionic devices. There is also a published roadmap to neuromorphic engineering featuring both analog and digital devices, mentioned in my April 18, 2014 posting.