Tag Archives: human brain

Memristors with better mimicry of synapses

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper is behind a paywall.

The researchers have made images illustrating their work available,

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

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

That’s all folks.

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

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

UK

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

China

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

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

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

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

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

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

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

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

Korea

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

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

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

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

This paper is behind a paywall.

Artificial synapse courtesy of nanowires

It looks like a popsicle to me,

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

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

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

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

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

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

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

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

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

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

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

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

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

Courtesy: Polytecnico di Torino

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

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

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

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

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

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

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

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

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

This paper is open access.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

There is a visual representation of this artificial synapse,

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

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

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

This paper is behind a paywall.

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

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

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

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

3D printed human corneas

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

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

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

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

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

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

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

Here are the proud researchers with their cornea,

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper is behind a paywall.

A 3D printed copy of your brain

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here’s an image illustrating the work,

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

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

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

This paper appears to be open access.

A tangential Brad Pitt connection

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

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

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

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

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

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

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

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

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

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

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

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

That’s it for 3D printing today.

Santiago Ramón y Cajal and the butterflies of the soul

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

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

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

SUMMER PROGRAMS

ONGOING

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

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

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

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

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

EVENTS

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

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

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

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

Come back to see our exciting lineup for the fall!

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

Origins and Fates

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper is behind a paywall.

Less is more—a superconducting synapse

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

NIST has prepared an animation illustrating the research,

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

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

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

This paper is open access.

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

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

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

New path to viable memristor/neuristor?

I first stumbled onto memristors and the possibility of brain-like computing sometime in 2008 (around the time that R. Stanley Williams and his team at HP Labs first published the results of their research linking Dr. Leon Chua’s memristor theory to their attempts to shrink computer chips). In the almost 10 years since, scientists have worked hard to utilize memristors in the field of neuromorphic (brain-like) engineering/computing.

A January 22, 2018 news item on phys.org describes the latest work,

When it comes to processing power, the human brain just can’t be beat.

Packed within the squishy, football-sized organ are somewhere around 100 billion neurons. At any given moment, a single neuron can relay instructions to thousands of other neurons via synapses—the spaces between neurons, across which neurotransmitters are exchanged. There are more than 100 trillion synapses that mediate neuron signaling in the brain, strengthening some connections while pruning others, in a process that enables the brain to recognize patterns, remember facts, and carry out other learning tasks, at lightning speeds.

Researchers in the emerging field of “neuromorphic computing” have attempted to design computer chips that work like the human brain. Instead of carrying out computations based on binary, on/off signaling, like digital chips do today, the elements of a “brain on a chip” would work in an analog fashion, exchanging a gradient of signals, or “weights,” much like neurons that activate in various ways depending on the type and number of ions that flow across a synapse.

In this way, small neuromorphic chips could, like the brain, efficiently process millions of streams of parallel computations that are currently only possible with large banks of supercomputers. But one significant hangup on the way to such portable artificial intelligence has been the neural synapse, which has been particularly tricky to reproduce in hardware.

Now engineers at MIT [Massachusetts Institute of Technology] have designed an artificial synapse in such a way that they can precisely control the strength of an electric current flowing across it, similar to the way ions flow between neurons. The team has built a small chip with artificial synapses, made from silicon germanium. In simulations, the researchers found that the chip and its synapses could be used to recognize samples of handwriting, with 95 percent accuracy.

A January 22, 2018 MIT news release by Jennifer Chua (also on EurekAlert), which originated the news item, provides more detail about the research,

The design, published today [January 22, 2018] in the journal Nature Materials, is a major step toward building portable, low-power neuromorphic chips for use in pattern recognition and other learning tasks.

The research was led by Jeehwan Kim, the Class of 1947 Career Development Assistant Professor in the departments of Mechanical Engineering and Materials Science and Engineering, and a principal investigator in MIT’s Research Laboratory of Electronics and Microsystems Technology Laboratories. His co-authors are Shinhyun Choi (first author), Scott Tan (co-first author), Zefan Li, Yunjo Kim, Chanyeol Choi, and Hanwool Yeon of MIT, along with Pai-Yu Chen and Shimeng Yu of Arizona State University.

Too many paths

Most neuromorphic chip designs attempt to emulate the synaptic connection between neurons using two conductive layers separated by a “switching medium,” or synapse-like space. When a voltage is applied, ions should move in the switching medium to create conductive filaments, similarly to how the “weight” of a synapse changes.

But it’s been difficult to control the flow of ions in existing designs. Kim says that’s because most switching mediums, made of amorphous materials, have unlimited possible paths through which ions can travel — a bit like Pachinko, a mechanical arcade game that funnels small steel balls down through a series of pins and levers, which act to either divert or direct the balls out of the machine.

Like Pachinko, existing switching mediums contain multiple paths that make it difficult to predict where ions will make it through. Kim says that can create unwanted nonuniformity in a synapse’s performance.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” Kim says. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects. This stream is changing, and it’s hard to control. That’s the biggest problem — nonuniformity of the artificial synapse.”

A perfect mismatch

Instead of using amorphous materials as an artificial synapse, Kim and his colleagues looked to single-crystalline silicon, a defect-free conducting material made from atoms arranged in a continuously ordered alignment. The team sought to create a precise, one-dimensional line defect, or dislocation, through the silicon, through which ions could predictably flow.

To do so, the researchers started with a wafer of silicon, resembling, at microscopic resolution, a chicken-wire pattern. They then grew a similar pattern of silicon germanium — a material also used commonly in transistors — on top of the silicon wafer. Silicon germanium’s lattice is slightly larger than that of silicon, and Kim found that together, the two perfectly mismatched materials can form a funnel-like dislocation, creating a single path through which ions can flow.

The researchers fabricated a neuromorphic chip consisting of artificial synapses made from silicon germanium, each synapse measuring about 25 nanometers across. They applied voltage to each synapse and found that all synapses exhibited more or less the same current, or flow of ions, with about a 4 percent variation between synapses — a much more uniform performance compared with synapses made from amorphous material.

They also tested a single synapse over multiple trials, applying the same voltage over 700 cycles, and found the synapse exhibited the same current, with just 1 percent variation from cycle to cycle.

“This is the most uniform device we could achieve, which is the key to demonstrating artificial neural networks,” Kim says.

Writing, recognized

As a final test, Kim’s team explored how its device would perform if it were to carry out actual learning tasks — specifically, recognizing samples of handwriting, which researchers consider to be a first practical test for neuromorphic chips. Such chips would consist of “input/hidden/output neurons,” each connected to other “neurons” via filament-based artificial synapses.

Scientists believe such stacks of neural nets can be made to “learn.” For instance, when fed an input that is a handwritten ‘1,’ with an output that labels it as ‘1,’ certain output neurons will be activated by input neurons and weights from an artificial synapse. When more examples of handwritten ‘1s’ are fed into the same chip, the same output neurons may be activated when they sense similar features between different samples of the same letter, thus “learning” in a fashion similar to what the brain does.

Kim and his colleagues ran a computer simulation of an artificial neural network consisting of three sheets of neural layers connected via two layers of artificial synapses, the properties of which they based on measurements from their actual neuromorphic chip. They fed into their simulation tens of thousands of samples from a handwritten recognition dataset commonly used by neuromorphic designers, and found that their neural network hardware recognized handwritten samples 95 percent of the time, compared to the 97 percent accuracy of existing software algorithms.

The team is in the process of fabricating a working neuromorphic chip that can carry out handwriting-recognition tasks, not in simulation but in reality. Looking beyond handwriting, Kim says the team’s artificial synapse design will enable much smaller, portable neural network devices that can perform complex computations that currently are only possible with large supercomputers.

“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” Kim says. “This opens a stepping stone to produce real artificial hardware.”

This research was supported in part by the National Science Foundation.

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

SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations by Shinhyun Choi, Scott H. Tan, Zefan Li, Yunjo Kim, Chanyeol Choi, Pai-Yu Chen, Hanwool Yeon, Shimeng Yu, & Jeehwan Kim. Nature Materials (2018) doi:10.1038/s41563-017-0001-5 Published online: 22 January 2018

This paper is behind a paywall.

For the curious I have included a number of links to recent ‘memristor’ postings here,

January 22, 2018: Memristors at Masdar

January 3, 2018: Mott memristor

August 24, 2017: Neuristors and brainlike computing

June 28, 2017: Dr. Wei Lu and bio-inspired ‘memristor’ chips

May 2, 2017: Predicting how a memristor functions

December 30, 2016: Changing synaptic connectivity with a memristor

December 5, 2016: The memristor as computing device

November 1, 2016: The memristor as the ‘missing link’ in bioelectronic medicine?

You can find more by using ‘memristor’ as the search term in the blog search function or on the search engine of your choice.

Thanks for the memory: the US National Institute of Standards and Technology (NIST) and memristors

In January 2018 it seemed like I was tripping across a lot of memristor stories . This came from a January 19, 2018 news item on Nanowerk,

In the race to build a computer that mimics the massive computational power of the human brain, researchers are increasingly turning to memristors, which can vary their electrical resistance based on the memory of past activity. Scientists at the National Institute of Standards and Technology (NIST) have now unveiled the long-mysterious inner workings of these semiconductor elements, which can act like the short-term memory of nerve cells.

A January 18, 2018 NIST news release (also on EurekAlert), which originated the news item, fills in the details,

Just as the ability of one nerve cell to signal another depends on how often the cells have communicated in the recent past, the resistance of a memristor depends on the amount of current that recently flowed through it. Moreover, a memristor retains that memory even when electrical power is switched off.

But despite the keen interest in memristors, scientists have lacked a detailed understanding of how these devices work and have yet to develop a standard toolset to study them.

Now, NIST scientists have identified such a toolset and used it to more deeply probe how memristors operate. Their findings could lead to more efficient operation of the devices and suggest ways to minimize the leakage of current.

Brian Hoskins of NIST and the University of California, Santa Barbara, along with NIST scientists Nikolai Zhitenev, Andrei Kolmakov, Jabez McClelland and their colleagues from the University of Maryland’s NanoCenter (link is external) in College Park and the Institute for Research and Development in Microtechnologies in Bucharest, reported the findings (link is external) in a recent Nature Communications.

To explore the electrical function of memristors, the team aimed a tightly focused beam of electrons at different locations on a titanium dioxide memristor. The beam knocked free some of the device’s electrons, which formed ultrasharp images of those locations. The beam also induced four distinct currents to flow within the device. The team determined that the currents are associated with the multiple interfaces between materials in the memristor, which consists of two metal (conducting) layers separated by an insulator.

“We know exactly where each of the currents are coming from because we are controlling the location of the beam that is inducing those currents,” said Hoskins.

In imaging the device, the team found several dark spots—regions of enhanced conductivity—which indicated places where current might leak out of the memristor during its normal operation. These leakage pathways resided outside the memristor’s core—where it switches between the low and high resistance levels that are useful in an electronic device. The finding suggests that reducing the size of a memristor could minimize or even eliminate some of the unwanted current pathways. Although researchers had suspected that might be the case, they had lacked experimental guidance about just how much to reduce the size of the device.

Because the leakage pathways are tiny, involving distances of only 100 to 300 nanometers, “you’re probably not going to start seeing some really big improvements until you reduce dimensions of the memristor on that scale,” Hoskins said.

To their surprise, the team also found that the current that correlated with the memristor’s switch in resistance didn’t come from the active switching material at all, but the metal layer above it. The most important lesson of the memristor study, Hoskins noted, “is that you can’t just worry about the resistive switch, the switching spot itself, you have to worry about everything around it.” The team’s study, he added, “is a way of generating much stronger intuition about what might be a good way to engineer memristors.”

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

Stateful characterization of resistive switching TiO2 with electron beam induced currents by Brian D. Hoskins, Gina C. Adam, Evgheni Strelcov, Nikolai Zhitenev, Andrei Kolmakov, Dmitri B. Strukov, & Jabez J. McClelland. Nature Communications 8, Article number: 1972 (2017) doi:10.1038/s41467-017-02116-9 Published online: 07 December 2017

This is an open access paper.

It might be my imagination but it seemed like a lot of papers from 2017 were being publicized in early 2018.

Finally, I borrowed much of my headline from the NIST’s headline for its news release, specifically, “Thanks for the memory,” which is a rather old song,

Bob Hope and Shirley Ross in “The Big Broadcast of 1938.”

A bioengineered robot hand with its own nervous system: machine/flesh and a job opening

A November 14, 2017 news item on phys.org announces a grant for a research project which will see engineered robot hands combined with regenerative medicine to imbue neuroprosthetic hands with the sense of touch,

The sense of touch is often taken for granted. For someone without a limb or hand, losing that sense of touch can be devastating. While highly sophisticated prostheses with complex moving fingers and joints are available to mimic almost every hand motion, they remain frustratingly difficult and unnatural for the user. This is largely because they lack the tactile experience that guides every movement. This void in sensation results in limited use or abandonment of these very expensive artificial devices. So why not make a prosthesis that can actually “feel” its environment?

That is exactly what an interdisciplinary team of scientists from Florida Atlantic University and the University of Utah School of Medicine aims to do. They are developing a first-of-its-kind bioengineered robotic hand that will grow and adapt to its environment. This “living” robot will have its own peripheral nervous system directly linking robotic sensors and actuators. FAU’s College of Engineering and Computer Science is leading the multidisciplinary team that has received a four-year, $1.3 million grant from the National Institute of Biomedical Imaging and Bioengineering of the [US] National Institutes of Health for a project titled “Virtual Neuroprosthesis: Restoring Autonomy to People Suffering from Neurotrauma.”

A November14, 2017 Florida Atlantic University (FAU) news release by Gisele Galoustian, which originated the news item, goes into more detail,

With expertise in robotics, bioengineering, behavioral science, nerve regeneration, electrophysiology, microfluidic devices, and orthopedic surgery, the research team is creating a living pathway from the robot’s touch sensation to the user’s brain to help amputees control the robotic hand. A neuroprosthesis platform will enable them to explore how neurons and behavior can work together to regenerate the sensation of touch in an artificial limb.

At the core of this project is a cutting-edge robotic hand and arm developed in the BioRobotics Laboratory in FAU’s College of Engineering and Computer Science. Just like human fingertips, the robotic hand is equipped with numerous sensory receptors that respond to changes in the environment. Controlled by a human, it can sense pressure changes, interpret the information it is receiving and interact with various objects. It adjusts its grip based on an object’s weight or fragility. But the real challenge is figuring out how to send that information back to the brain using living residual neural pathways to replace those that have been damaged or destroyed by trauma.

“When the peripheral nerve is cut or damaged, it uses the rich electrical activity that tactile receptors create to restore itself. We want to examine how the fingertip sensors can help damaged or severed nerves regenerate,” said Erik Engeberg, Ph.D., principal investigator, an associate professor in FAU’s Department of Ocean and Mechanical Engineering, and director of FAU’s BioRobotics Laboratory. “To accomplish this, we are going to directly connect these living nerves in vitro and then electrically stimulate them on a daily basis with sensors from the robotic hand to see how the nerves grow and regenerate while the hand is operated by limb-absent people.”

For the study, the neurons will not be kept in conventional petri dishes. Instead, they will be placed in  biocompatible microfluidic chambers that provide a nurturing environment mimicking the basic function of living cells. Sarah E. Du, Ph.D., co-principal investigator, an assistant professor in FAU’s Department of Ocean and Mechanical Engineering, and an expert in the emerging field of microfluidics, has developed these tiny customized artificial chambers with embedded micro-electrodes. The research team will be able to stimulate the neurons with electrical impulses from the robot’s hand to help regrowth after injury. They will morphologically and electrically measure in real-time how much neural tissue has been restored.

Jianning Wei, Ph.D., co-principal investigator, an associate professor of biomedical science in FAU’s Charles E. Schmidt College of Medicine, and an expert in neural damage and regeneration, will prepare the neurons in vitro, observe them grow and see how they fare and regenerate in the aftermath of injury. This “virtual” method will give the research team multiple opportunities to test and retest the nerves without any harm to subjects.

Using an electroencephalogram (EEG) to detect electrical activity in the brain, Emmanuelle Tognoli, Ph.D., co-principal investigator, associate research professor in FAU’s Center for Complex Systems and Brain Sciences in the Charles E. Schmidt College of Science, and an expert in electrophysiology and neural, behavioral, and cognitive sciences, will examine how the tactile information from the robotic sensors is passed onto the brain to distinguish scenarios with successful or unsuccessful functional restoration of the sense of touch. Her objective: to understand how behavior helps nerve regeneration and how this nerve regeneration helps the behavior.

Once the nerve impulses from the robot’s tactile sensors have gone through the microfluidic chamber, they are sent back to the human user manipulating the robotic hand. This is done with a special device that converts the signals coming from the microfluidic chambers into a controllable pressure at a cuff placed on the remaining portion of the amputated person’s arm. Users will know if they are squeezing the object too hard or if they are losing their grip.

Engeberg also is working with Douglas T. Hutchinson, M.D., co-principal investigator and a professor in the Department of Orthopedics at the University of Utah School of Medicine, who specializes in hand and orthopedic surgery. They are developing a set of tasks and behavioral neural indicators of performance that will ultimately reveal how to promote a healthy sensation of touch in amputees and limb-absent people using robotic devices. The research team also is seeking a post-doctoral researcher with multi-disciplinary experience to work on this breakthrough project.

Here’s more about the job opportunity from the FAU BioRobotics Laboratory job posting, (I checked on January 30, 2018 and it seems applications are still being accepted.)

Post-doctoral Opportunity

Dated Posted: Oct. 13, 2017

The BioRobotics Lab at Florida Atlantic University (FAU) invites applications for a NIH NIBIB-funded Postdoctoral position to develop a Virtual Neuroprosthesis aimed at providing a sense of touch in amputees and limb-absent people.

Candidates should have a Ph.D. in one of the following degrees: mechanical engineering, electrical engineering, biomedical engineering, bioengineering or related, with interest and/or experience in transdisciplinary work at the intersection of robotic hands, biology, and biomedical systems. Prior experience in the neural field will be considered an advantage, though not a necessity. Underrepresented minorities and women are warmly encouraged to apply.

The postdoctoral researcher will be co-advised across the department of Mechanical Engineering and the Center for Complex Systems & Brain Sciences through an interdisciplinary team whose expertise spans Robotics, Microfluidics, Behavioral and Clinical Neuroscience and Orthopedic Surgery.

The position will be for one year with a possibility of extension based on performance. Salary will be commensurate with experience and qualifications. Review of applications will begin immediately and continue until the position is filled.

The application should include:

  1. a cover letter with research interests and experiences,
  2. a CV, and
  3. names and contact information for three professional references.

Qualified candidates can contact Erik Engeberg, Ph.D., Associate Professor, in the FAU Department of Ocean and Mechanical Engineering at eengeberg@fau.edu. Please reference AcademicKeys.com in your cover letter when applying for or inquiring about this job announcement.

You can find the apply button on this page. Good luck!