Tag Archives: neurons

Memristors, it’s all about the oxides

I have one research announcement from China and another from the Netherlands, both of which concern memristors and oxides.

China

A May 17, 2021 news item on Nanowerk announces work, which suggests that memristors may not need to rely solely on oxides but could instead utilize light more gainfully,

Scientists are getting better at making neuron-like junctions for computers that mimic the human brain’s random information processing, storage and recall. Fei Zhuge of the Chinese Academy of Sciences and colleagues reviewed the latest developments in the design of these ‘memristors’ for the journal Science and Technology of Advanced Materials …

Computers apply artificial intelligence programs to recall previously learned information and make predictions. These programs are extremely energy- and time-intensive: typically, vast volumes of data must be transferred between separate memory and processing units. To solve this issue, researchers have been developing computer hardware that allows for more random and simultaneous information transfer and storage, much like the human brain.

Electronic circuits in these ‘neuromorphic’ computers include memristors that resemble the junctions between neurons called synapses. Energy flows through a material from one electrode to another, much like a neuron firing a signal across the synapse to the next neuron. Scientists are now finding ways to better tune this intermediate material so the information flow is more stable and reliable.

I had no success locating the original news release, which originated the news item, but have found this May 17, 2021 news item on eedesignit.com, which provides the remaining portion of the news release.

“Oxides are the most widely used materials in memristors,” said Zhuge. “But oxide memristors have unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve this.”

Memristors are usually made of an oxide-based material sandwiched between two electrodes. Researchers are getting better results when they combine two or more layers of different oxide-based materials between the electrodes. When an electrical current flows through the network, it induces ions to drift within the layers. The ions’ movements ultimately change the memristor’s resistance, which is necessary to send or stop a signal through the junction.

Memristors can be tuned further by changing the compounds used for electrodes or by adjusting the intermediate oxide-based materials. Zhuge and his team are currently developing optoelectronic neuromorphic computers based on optically-controlled oxide memristors. Compared to electronic memristors, photonic ones are expected to have higher operation speeds and lower energy consumption. They could be used to construct next generation artificial visual systems with high computing efficiency.

Now for a picture that accompanied the news release, which follows,

Fig. The all-optically controlled memristor developed for optoelectronic neuromorphic computing (Image by NIMTE)

Here’s the February 7, 2021 Ningbo Institute of Materials Technology and Engineering (NIMTE) press release featuring this work and a more technical description,

A research group led by Prof. ZHUGE Fei at the Ningbo Institute of Materials Technology and Engineering (NIMTE) of the Chinese Academy of Sciences (CAS) developed an all-optically controlled (AOC) analog memristor, whose memconductance can be reversibly tuned by varying only the wavelength of the controlling light.

As the next generation of artificial intelligence (AI), neuromorphic computing (NC) emulates the neural structure and operation of the human brain at the physical level, and thus can efficiently perform multiple advanced computing tasks such as learning, recognition and cognition.

Memristors are promising candidates for NC thanks to the feasibility of high-density 3D integration and low energy consumption. Among them, the emerging optoelectronic memristors are competitive by virtue of combining the advantages of both photonics and electronics. However, the reversible tuning of memconductance depends highly on the electric excitation, which have severely limited the development and application of optoelectronic NC.

To address this issue, researchers at NIMTE proposed a bilayered oxide AOC memristor, based on the relatively mature semiconductor material InGaZnO and a memconductance tuning mechanism of light-induced electron trapping and detrapping.

The traditional electrical memristors require strong electrical stimuli to tune their memconductance, leading to high power consumption, a large amount of Joule heat, microstructural change triggered by the Joule heat, and even high crosstalk in memristor crossbars.

On the contrary, the developed AOC memristor does not involve microstructure changes, and can operate upon weak light irradiation with light power density of only 20 μW cm-2, which has provided a new approach to overcome the instability of the memristor.

Specifically, the AOC memristor can serve as an excellent synaptic emulator and thus mimic spike-timing-dependent plasticity (STDP) which is an important learning rule in the brain, indicating its potential applications in AOC spiking neural networks for high-efficiency optoelectronic NC.

Moreover, compared to purely optical computing, the optoelectronic computing using our AOC memristor showed higher practical feasibility, on account of the simple structure and fabrication process of the device.

The study may shed light on the in-depth research and practical application of optoelectronic NC, and thus promote the development of the new generation of AI.

This work was supported by the National Natural Science Foundation of China (No. 61674156 and 61874125), the Strategic Priority Research Program of Chinese Academy of Sciences (No. XDB32050204), and the Zhejiang Provincial Natural Science Foundation of China (No. LD19E020001).

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

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence by Jingrui Wang, Xia Zhuge & Fei Zhuge. Science and Technology of Advanced Materials Volume 22, 2021 – Issue 1 Pages 326-344 DOI: https://doi.org/10.1080/14686996.2021.1911277 Published online:14 May 2021

This paper appears to be open access.

Netherlands

In this case, a May 18, 2021 news item on Nanowerk marries oxides to spintronics,

Classic computers use binary values (0/1) to perform. By contrast, our brain cells can use more values to operate, making them more energy-efficient than computers. This is why scientists are interested in neuromorphic (brain-like) computing.

Physicists from the University of Groningen (the Netherlands) have used a complex oxide to create elements comparable to the neurons and synapses in the brain using spins, a magnetic property of electrons.

The press release, which follows, was accompanied by this image illustrating the work,

Caption: Schematic of the proposed device structure for neuromorphic spintronic memristors. The write path is between the terminals through the top layer (black dotted line), the read path goes through the device stack (red dotted line). The right side of the figure indicates how the choice of substrate dictates whether the device will show deterministic or probabilistic behaviour. Credit: Banerjee group, University of Groningen

A May 18, 2021 University of Groningen press release (also on EurekAlert), which originated the news item, adds more ‘spin’ to the story,

Although computers can do straightforward calculations much faster than humans, our brains outperform silicon machines in tasks like object recognition. Furthermore, our brain uses less energy than computers. Part of this can be explained by the way our brain operates: whereas a computer uses a binary system (with values 0 or 1), brain cells can provide more analogue signals with a range of values.

Thin films

The operation of our brains can be simulated in computers, but the basic architecture still relies on a binary system. That is why scientist look for ways to expand this, creating hardware that is more brain-like, but will also interface with normal computers. ‘One idea is to create magnetic bits that can have intermediate states’, says Tamalika Banerjee, Professor of Spintronics of Functional Materials at the Zernike Institute for Advanced Materials, University of Groningen. She works on spintronics, which uses a magnetic property of electrons called ‘spin’ to transport, manipulate and store information.

In this study, her PhD student Anouk Goossens, first author of the paper, created thin films of a ferromagnetic metal (strontium-ruthenate oxide, SRO) grown on a substrate of strontium titanate oxide. The resulting thin film contained magnetic domains that were perpendicular to the plane of the film. ‘These can be switched more efficiently than in-plane magnetic domains’, explains Goossens. By adapting the growth conditions, it is possible to control the crystal orientation in the SRO. Previously, out-of-plane magnetic domains have been made using other techniques, but these typically require complex layer structures.

Magnetic anisotropy

The magnetic domains can be switched using a current through a platinum electrode on top of the SRO. Goossens: ‘When the magnetic domains are oriented perfectly perpendicular to the film, this switching is deterministic: the entire domain will switch.’ However, when the magnetic domains are slightly tilted, the response is probabilistic: not all the domains are the same, and intermediate values occur when only part of the crystals in the domain have switched.

By choosing variants of the substrate on which the SRO is grown, the scientists can control its magnetic anisotropy. This allows them to produce two different spintronic devices. ‘This magnetic anisotropy is exactly what we wanted’, says Goossens. ‘Probabilistic switching compares to how neurons function, while the deterministic switching is more like a synapse.’

The scientists expect that in the future, brain-like computer hardware can be created by combining these different domains in a spintronic device that can be connected to standard silicon-based circuits. Furthermore, probabilistic switching would also allow for stochastic computing, a promising technology which represents continuous values by streams of random bits. Banerjee: ‘We have found a way to control intermediate states, not just for memory but also for computing.’

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

Anisotropy and Current Control of Magnetization in SrRuO3/SrTiO3 Heterostructures for Spin-Memristors by A.S. Goossens, M.A.T. Leiviskä and T. Banerjee. Frontiers in Nanotechnology DOI: https://doi.org/10.3389/fnano.2021.680468 Published: 18 May 2021

This appears to be open access.

Impact of graphene flakes (nanoparticles) on neurons

This research suggests that graphene flakes might have an impact on anxiety-related behaviour. If I read the work correctly, the graphene flakes don’t exacerbate anxiety but, instead, may provide relief.

A March 10, 2021 news item on phys.org announces the research into graphene flakes and neurons (rat), Note: Links have been removed,

Effective, specific, with a reversible and non-harmful action: the identikit of the perfect biomaterial seems to correspond to graphene flakes, the subject of a new study carried out by SISSA—International School for Advanced Studies of Trieste, Catalan Institute of Nanoscience and Nanotechnology (ICN2) of Barcelona, and the National Graphene Institute of the University of Manchester, as part of the European Graphene Flagship project. This nanomaterial has demonstrated the ability to interact with the functions of the nervous system in vertebrates in a very specific manner, interrupting the building up of a pathological process that leads to anxiety-related behavior.

“We previously showed that when graphene flakes are delivered to neurons they interfere spontaneously with excitatory synapses by transiently preventing glutamate release from presynaptic terminals,” says Laura Ballerini of SISSA, the leader of the team that carried out the research study “Graphene oxide prevents lateral amygdala dysfunctional synaptic plasticity and reverts long lasting anxiety behavior in rats,” recently published in Biomaterials.

A March 10, 2021 Scuola Internazionale Superiore di Studi Avanzati (SISSA) press release (also on EurekAlert), which originated the news item, provides more detail,

“We investigated whether such a reduction in synaptic activity was sufficient to modify related behaviours, in particular the pathological ones that develop due to a transient and localised hyper-function of excitatory synapses”. This approach would fortify the strategy of selective and transient targeting of synapses to prevent the development of brain pathologies by using the so-called precise medicine treatments.

To test this hypothesis, the team focused on post-traumatic stress disorder (PTSD) and carried out the experiments in two phases, in vivo and in vitro.

“We analysed defensive behaviours caused in rats [emphasis mine] by the presence of a predator, using the exposure to cat odour, to induce an aversive memory” explains Audrey Franceschi Biagioni of SISSA, the first author of the study. “If exposed to the predator odour, the rat has a defensive response, holing up, and this experience is so well-imprinted in the memory, that when the animal is placed in the same context even six days later, the animal remembers the odour of the predator and acts the same protective behaviour. This is a well-known and consolidated model, that we used to reproduce a stress behaviour. Exposure to the predator can modify neuronal connections – a phenomenon that is technically known as plasticity – and increases synaptic activity in a specific area of the amygdala that therefore represented the target of our study to test the effects of the nanomaterial”.

Laura Ballerini adds: “We hypothesised that graphene flakes that we showed to temporarily inhibit excitatory synapses (without causing inflammation, damage to neurons or other side effects) could be injected in the lateral amygdala when the plasticity associated with memory was consolidated. If the nanomaterial was efficient in blocking excitatory synapses, it should inhibit plasticity and decrease the anxiety related response. And this is what happened: the animals that were administered with graphene flakes, after six days, “forgot” the anxiety related responses, rescuing their behaviour”.

The second part of the research was performed in vitro. “In vivo we could observe only behavioural changes and could not evaluate the impact of the graphene flakes on synapses,” explains Giada Cellot, researcher at SISSA and first author of the study together with Audrey Franceschi Biagioni. “In vitro experiments allowed to work on a simplified model, to get insight about the mechanisms through which the graphene flakes can interact with neurons. We used neuronal cultures obtained from the amygdala, the region of the brain where the stress response occurs, and we observed that the effects of nanomaterials were specific for the excitatory synapses and a short exposure to graphene flakes could prevent the pathological plasticity of the synapses”.

Thanks to these findings, graphene flakes have shown their potential as nanotools (biomedical tools composed of nanomaterials) that could act in a specific and reversible way on synaptic activity to interrupt a pathological process and therefore they might be used also to transport drugs or for other applications in the field of precision medicine.

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

Graphene oxide prevents lateral amygdala dysfunctional synaptic plasticity and reverts long lasting anxiety behavior in rats by Audrey Franceschi Biagionia1, Giada Cellot, Elisa Pati, Neus Lozano, Belén Ballesteros, Raffaele Casani, Norberto Cysne Coimbra, Kostas Kostarelos, Laura Ballerini. Biomaterials Volume 271, April 2021, 120749 DOI: https://doi.org/10.1016/j.biomaterials.2021.120749

This paper is open access.

Transplanting healthy neurons could be possible with walking molecules and 3D printing

A February 23, 2021 news item on ScienceDaily announces work which may lead to healing brain injuries and diseases,

Imagine if surgeons could transplant healthy neurons into patients living with neurodegenerative diseases or brain and spinal cord injuries. And imagine if they could “grow” these neurons in the laboratory from a patient’s own cells using a synthetic, highly bioactive material that is suitable for 3D printing.

By discovering a new printable biomaterial that can mimic properties of brain tissue, Northwestern University researchers are now closer to developing a platform capable of treating these conditions using regenerative medicine.

A February 22, 2021 Northwestern University news release (also received by email and available on EurekAlert) by Lila Reynolds, which originated the news item, delves further into self-assembling ‘walking’ molecules and the nanofibers resulting in a new material designed to promote the growth of healthy neurons,

A key ingredient to the discovery is the ability to control the self-assembly processes of molecules within the material, enabling the researchers to modify the structure and functions of the systems from the nanoscale to the scale of visible features. The laboratory of Samuel I. Stupp published a 2018 paper in the journal Science which showed that materials can be designed with highly dynamic molecules programmed to migrate over long distances and self-organize to form larger, “superstructured” bundles of nanofibers.

Now, a research group led by Stupp has demonstrated that these superstructures can enhance neuron growth, an important finding that could have implications for cell transplantation strategies for neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease, as well as spinal cord injury.

“This is the first example where we’ve been able to take the phenomenon of molecular reshuffling we reported in 2018 and harness it for an application in regenerative medicine,” said Stupp, the lead author on the study and the director of Northwestern’s Simpson Querrey Institute. “We can also use constructs of the new biomaterial to help discover therapies and understand pathologies.

Walking molecules and 3D printing

The new material is created by mixing two liquids that quickly become rigid as a result of interactions known in chemistry as host-guest complexes that mimic key-lock interactions among proteins, and also as the result of the concentration of these interactions in micron-scale regions through a long scale migration of “walking molecules.”

The agile molecules cover a distance thousands of times larger than themselves in order to band together into large superstructures. At the microscopic scale, this migration causes a transformation in structure from what looks like an uncooked chunk of ramen noodles into ropelike bundles.

“Typical biomaterials used in medicine like polymer hydrogels don’t have the capabilities to allow molecules to self-assemble and move around within these assemblies,” said Tristan Clemons, a research associate in the Stupp lab and co-first author of the paper with Alexandra Edelbrock, a former graduate student in the group. “This phenomenon is unique to the systems we have developed here.”

Furthermore, as the dynamic molecules move to form superstructures, large pores open that allow cells to penetrate and interact with bioactive signals that can be integrated into the biomaterials.

Interestingly, the mechanical forces of 3D printing disrupt the host-guest interactions in the superstructures and cause the material to flow, but it can rapidly solidify into any macroscopic shape because the interactions are restored spontaneously by self-assembly. This also enables the 3D printing of structures with distinct layers that harbor different types of neural cells in order to study their interactions.

Signaling neuronal growth

The superstructure and bioactive properties of the material could have vast implications for tissue regeneration. Neurons are stimulated by a protein in the central nervous system known as brain-derived neurotrophic factor (BDNF), which helps neurons survive by promoting synaptic connections and allowing neurons to be more plastic. BDNF could be a valuable therapy for patients with neurodegenerative diseases and injuries in the spinal cord but these proteins degrade quickly in the body and are expensive to produce.

One of the molecules in the new material integrates a mimic of this protein that activates its receptor known as Trkb, and the team found that neurons actively penetrate the large pores and populate the new biomaterial when the mimetic signal is present. This could also create an environment in which neurons differentiated from patient-derived stem cells mature before transplantation.

Now that the team has applied a proof of concept to neurons, Stupp believes he could now break into other areas of regenerative medicine by applying different chemical sequences to the material. Simple chemical changes in the biomaterials would allow them to provide signals for a wide range of tissues.

“Cartilage and heart tissue are very difficult to regenerate after injury or heart attacks, and the platform could be used to prepare these tissues in vitro from patient-derived cells,” Stupp said. “These tissues could then be transplanted to help restore lost functions. Beyond these interventions, the materials could be used to build organoids to discover therapies or even directly implanted into tissues for regeneration since they are biodegradable.”

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

Superstructured Biomaterials Formed by Exchange Dynamics and Host–Guest Interactions in Supramolecular Polymers by Alexandra N. Edelbrock, Tristan D. Clemons, Stacey M. Chin, Joshua J. W. Roan, Eric P. Bruckner, Zaida Álvarez, Jack F. Edelbrock, Kristen S. Wek, Samuel I. Stupp. Advanced Science DOI: https://doi.org/10.1002/advs.202004042 First published: 22 February 2021

This paper is open access.

Neuronal regenerative-interfaces made of cross-linked carbon nanotube films

If I understand this research rightly, they are creating a film made of carbon nanotubes that can stimulate the growth of nerve cells (neurons) thus creating a ‘living/nonliving’ hybrid or as they call it in the press release a ‘biosynthetic hybrid’.

An August 2, 2019 news item on Nanowerk introduces the research (Note 1: There seem to be some translation issues; Note 2: Links have been removed),

Carbon nanotubes able to take on the desired shapes thanks to a special chemical treatment, called crosslinking and, at the same time, able to function as substrata for the growth of nerve cells, finely tuning their growth and activity.

The research published in ACS Nano (“Chemically Cross-Linked Carbon Nanotube Films Engineered to Control Neuronal Signaling”), is a new and important step towards the construction of neuronal regenerative-interfaces to repair spinal injuries.

The study is the new achievement of a long-term and, in terms of results, successful collaboration between the scientists Laura Ballerini of SISSA (Scuola Internazionale Superiore di Studi Avanzati), Trieste, and Maurizio Prato of the University of Trieste. The work team has also been assisted by CIC biomaGUNE of San Sebastián, Spain.

Caption: Carbon nanotubes able to take on the desired shapes thanks to a special chemical treatment, called crosslinking and, at the same time, able to function as substrata for the growth of nerve cells, finely tuning their growth and activity. Credit: Rossana Rauti

An August 2, 2019 SISSA press release (also on EurekAlert), which originated the news item, adds detail,

The carbon nanotubes used in the research have been modified by appropriate chemical treatments: “For many years, in our laboratories we have been working on the chemical reactivity of carbon nanotubes, a fascinating but very difficult material to work. Thanks to our experience, we have crosslinked them or, to say it more clearly, we have treated the nanotubes so they could link themselves to one another thanks to specific chemical reactions. We have discovered that this procedure gives the material very interesting characteristics. For example, the material organises itself in a stable manner according to a precise shape, we choose: a tissue where nerve cells need to be planted, for example. Or around some electrodes” explains Professor Prato. “We know from previous research that nerve cells grow well on carbon nanotubes so they could be used as a surface to build hybrid devices to regenerate nerve tissues. It was necessary to ensure that this chemical modification did not compromise this process and study whether the interaction with neurons was altered”.

Towards biosynthetic hybrids

Professor Ballerini continues: “We have discovered that the chemical process has important effects because through this treatment we can modulate the activity of neurons, in terms of growth, adhesion and survival. These materials can also regulate the communication between neurons. We can say that the carpet of crosslinked carbon nanotubes interacts intensely and constructively with the nerve cells”. This interaction depends on how much the different carbon nanotubes are linked to each other, or rather crosslinked. The lower the link number among the nanotubes the higher the activity of neurons that grow on their surface. Through the chemical control of their properties, and of the links between them, it is possible to regulate the response of the neurons. Ballerini and Prato explain: “This is an intriguing result that emerges from the important and fruitful collaboration between our research groups involving advanced research in chemistry, nanoscience and neurobiology . This study provides a further step in the design of future biosynthetic hybrids to recover injured nerve tissues functions”.

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

Chemically Cross-Linked Carbon Nanotube Films Engineered to Control Neuronal Signaling by Myriam Barrejón, Rossana Rauti, Laura Ballerini, Maurizio Prato. ACS Nano2019 XXXXXXXXXX-XXX Publication Date:July 22, 2019 DOI: https://doi.org/10.1021/acsnano.9b02429 Copyright © 2019 American Chemical Society

This paper is behind a paywall.

Repairing brain circuits using nanotechnology

A July 30, 2019 news item on Nanowerk announces some neuroscience research (they used animal models) that could prove helpful with neurodegenerative diseases,

Working with mouse and human tissue, Johns Hopkins Medicine researchers report new evidence that a protein pumped out of some — but not all — populations of “helper” cells in the brain, called astrocytes, plays a specific role in directing the formation of connections among neurons needed for learning and forming new memories.

Using mice genetically engineered and bred with fewer such connections, the researchers conducted proof-of-concept experiments that show they could deliver corrective proteins via nanoparticles to replace the missing protein needed for “road repairs” on the defective neural highway.

Since such connective networks are lost or damaged by neurodegenerative diseases such as Alzheimer’s or certain types of intellectual disability, such as Norrie disease, the researchers say their findings advance efforts to regrow and repair the networks and potentially restore normal brain function.

A July 30, 2019 Johns Hopkins University School of Medicine news release (also on EurekAlert) provides more detail about the work (Note: A link has been removed),

“We are looking at the fundamental biology of how astrocytes function, but perhaps have discovered a new target for someday intervening in neurodegenerative diseases with novel therapeutics,” says Jeffrey Rothstein, M.D., Ph.D., the John W. Griffin Director of the Brain Science Institute and professor of neurology at the Johns Hopkins University School of Medicine.

“Although astrocytes appear to all look alike in the brain, we had an inkling that they might have specialized roles in the brain due to regional differences in the brain’s function and because of observed changes in certain diseases,” says Rothstein. “The hope is that learning to harness the individual differences in these distinct populations of astrocytes may allow us to direct brain development or even reverse the effects of certain brain conditions, and our current studies have advanced that hope.”

In the brain, astrocytes are the support cells that act as guides to direct new cells, promote chemical signaling, and clean up byproducts of brain cell metabolism.

Rothstein’s team focused on a particular astrocyte protein, glutamate transporter-1, which previous studies suggested was lost from astrocytes in certain parts of brains with neurodegenerative diseases. Like a biological vacuum cleaner, the protein normally sucks up the chemical “messenger” glutamate from the spaces between neurons after a message is sent to another cell, a step required to end the transmission and prevent toxic levels of glutamate from building up.

When these glutamate transporters disappear from certain parts of the brain — such as the motor cortex and spinal cord in people with amyotrophic lateral sclerosis (ALS) — glutamate hangs around much too long, sending messages that overexcite and kill the cells.

To figure out how the brain decides which cells need the glutamate transporters, Rothstein and colleagues focused on the region of DNA in front of the gene that typically controls the on-off switch needed to manufacture the protein. They genetically engineered mice to glow red in every cell where the gene is activated.

Normally, the glutamate transporter is turned on in all astrocytes. But, by using between 1,000- and 7,000-bit segments of DNA code from the on-off switch for glutamate, all the cells in the brain glowed red, including the neurons. It wasn’t until the researchers tried the largest sequence of an 8,300-bit DNA code from this location that the researchers began to see some selection in red cells. These red cells were all astrocytes but only in certain layers of the brain’s cortex in mice.

Because they could identify these “8.3 red astrocytes,” the researchers thought they might have a specific function different than other astrocytes in the brain. To find out more precisely what these 8.3 red astrocytes do in the brain, the researchers used a cell-sorting machine to separate the red astrocytes from the uncolored ones in mouse brain cortical tissue, and then identified which genes were turned on to much higher than usual levels in the red compared to the uncolored cell populations. The researchers found that the 8.3 red astrocytes turn on high levels of a gene that codes for a different protein known as Norrin.

Rothstein’s team took neurons from normal mouse brains, treated them with Norrin, and found that those neurons grew more of the “branches” — or extensions — used to transmit chemical messages among brain cells. Then, Rothstein says, the researchers looked at the brains of mice engineered to lack Norrin, and saw that these neurons had fewer branches than in healthy mice that made Norrin.

In another set of experiments, the research team took the DNA code for Norrin plus the 8,300 “location” DNA and assembled them into deliverable nanoparticles. When they injected the Norrin nanoparticles into the brains of mice engineered without Norrin, the neurons in these mice began to quickly grow many more branches, a process suggesting repair to neural networks. They repeated these experiments with human neurons too.

Rothstein notes that mutations in the Norrin protein that reduce levels of the protein in people cause Norrie disease — a rare, genetic disorder that can lead to blindness in infancy and intellectual disability. Because the researchers were able to grow new branches for communication, they believe it may one day be possible to use Norrin to treat some types of intellectual disabilities such as Norrie disease.

For their next steps, the researchers are investigating if Norrin can repair connections in the brains of animal models with neurodegenerative diseases, and in preparation for potential success, Miller [sic] and Rothstein have submitted a patent for Norrin.

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

Molecularly defined cortical astroglia subpopulation modulates neurons via secretion of Norrin by Sean J. Miller, Thomas Philips, Namho Kim, Raha Dastgheyb, Zhuoxun Chen, Yi-Chun Hsieh, J. Gavin Daigle, Malika Datta, Jeannie Chew, Svetlana Vidensky, Jacqueline T. Pham, Ethan G. Hughes, Michael B. Robinson, Rita Sattler, Raju Tomer, Jung Soo Suk, Dwight E. Bergles, Norman Haughey, Mikhail Pletnikov, Justin Hanes & Jeffrey D. Rothstein. Nature Neuroscience volume 22, pages741–752 (2019) DOI: https://doi.org/10.1038/s41593-019-0366-7 Published: 01 April 2019 Issue Date: May 2019

This paper is behind a paywall.

Memristors with better mimicry of synapses

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

This paper is behind a paywall.

The researchers have made images illustrating their work available,

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

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

That’s all folks.

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.

Non-viral ocular gene therapy with gold nanoparticles and femtosecond lasers

I love the stylistic choice the writer made (pay special attention to the second paragraph) when producing this November 19, 2018 Polytechnique Montréal news release (also on EurekAlert),

A scientific breakthrough by Professor Michel Meunier of Polytechnique Montréal and his collaborators offers hope for people with glaucoma, retinitis or macular degeneration.

In January 2009, the life of engineer Michel Meunier, a professor at Polytechnique Montréal, changed dramatically. Like others, he had observed that the extremely short pulse of a femtosecond laser (0.000000000000001 second) could make nanometre-sized holes appear in silicon when it was covered by gold nanoparticles. But this researcher, recognized internationally for his skills in laser and nanotechnology, decided to go a step further with what was then just a laboratory curiosity. He wondered if it was possible to go from silicon to living matter, from inorganic to organic. Could the gold nanoparticles and the femtosecond laser, this “light scalpel,” reproduce the same phenomenon with living cells?

A very pretty image illustrating the work,

Caption: Gold nanoparticles, which act like “nanolenses,” concentrate the energy produced by the extremely short pulse of a femtosecond laser to create a nanoscale incision on the surface of the eye’s retina cells. This technology, which preserves cell integrity, can be used to effectively inject drugs or genes into specific areas of the eye, offering new hope to people with glaucoma, retinitis or macular degeneration. Credit and Copyright: Polytechnique Montréal

The news release goes on to describe the technology in more detail,

Professor Meunier started working on cells in vitro in his Polytechnique laboratory. The challenge was to make a nanometric incision in the cells’ extracellular membrane without damaging it. Using gold nanoparticles that acted as “nanolenses,” Professor Meunier realized that it was possible to concentrate the light energy coming from the laser at a wavelength of 800 nanometres. Since there is very little energy absorption by the cells at this wavelength, their integrity is preserved. Mission accomplished!

Based on this finding, Professor Meunier decided to work on cells in vivo, cells that are part of a complex living cell structure, such as the eye for example.

The eye and the light scalpel

In April 2012, Professor Meunier met Przemyslaw Sapieha, an internationally renowned eye specialist, particularly recognized for his work on the retina. “Mike”, as he goes by, is a professor in the Department of Ophthalmology at Université de Montréal and a researcher at Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Est-de-l’Île-de-Montréal. He immediately saw the potential of this new technology and everything that could be done in the eye if you could block the ripple effect that occurs following a trigger that leads to glaucoma or macular degeneration, for example, by injecting drugs, proteins or even genes.

Using a femtosecond laser to treat the eye–a highly specialized and fragile organ–is very complex, however. The eye is part of the central nervous system, and therefore many of the cells or families of cells that compose it are neurons. And when a neuron dies, it does not regenerate like other cells do. Mike Sapieha’s first task was therefore to ensure that a femtosecond laser could be used on one or several neurons without affecting them. This is what is referred to as “proof of concept.”

Proof of concept

Mike and Michel called on biochemistry researcher Ariel Wilson, an expert in eye structures and vision mechanisms, as well as Professor Santiago Costantino and his team from the Department of Ophthalmology at Université de Montréal and the CIUSSS de l’Est-de-l’Île-de-Montréal for their expertise in biophotonics. The team first decided to work on healthy cells, because they are better understood than sick cells. They injected gold nanoparticles combined with antibodies to target specific neuronal cells in the eye, and then waited for the nanoparticles to settle around the various neurons or families of neurons, such as the retina. Following the bright flash generated by the femtosecond laser, the expected phenomenon occurred: small holes appeared in the cells of the eye’s retina, making it possible to effectively inject drugs or genes in specific areas of the eye. It was another victory for Michel Meunier and his collaborators, with these conclusive results now opening the path to new treatments.

The key feature of the technology developed by the researchers from Polytechnique and CIUSSS de l’Est-de-l’Île-de-Montréal is its extreme precision. With the use of functionalized gold nanoparticles, the light scalpel makes it possible to precisely locate the family of cells where the doctor will have to intervene.

Having successfully demonstrated proof of concept, Professor Meunier and his team filed a patent application in the United States. This tremendous work was also the subject of a paper reviewed by an impressive reading committee and published in the renowned journal Nano Letters in October 2018.

While there is still a lot of research to be done–at least 10 years’ worth, first on animals and then on humans–this technology could make all the difference in an aging population suffering from eye deterioration for which there are still no effective long-term treatments. It also has the advantage of avoiding the use of viruses commonly employed in gene therapy. These researchers are looking at applications of this technology in all eye diseases, but more particularly in glaucoma, retinitis and macular degeneration.

This light scalpel is unprecedented.

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

In Vivo Laser-Mediated Retinal Ganglion Cell Optoporation Using KV1.1 Conjugated Gold Nanoparticles by Ariel M. Wilson, Javier Mazzaferri, Éric Bergeron, Sergiy Patskovsky, Paule Marcoux-Valiquette, Santiago Costantino, Przemyslaw Sapieha, Michel Meunier. Nano Lett.201818116981-6988 DOI: https://doi.org/10.1021/acs.nanolett.8b02896 Publication Date: October 4, 2018  Copyright © 2018 American Chemical Society

This paper is behind a paywall.

Artificial synapse based on tantalum oxide from Korean researchers

This memristor story comes from South Korea as we progress on the way to neuromorphic computing (brainlike computing). A Sept. 7, 2018 news item on ScienceDaily makes the announcement,

A research team led by Director Myoung-Jae Lee from the Intelligent Devices and Systems Research Group at DGIST (Daegu Gyeongbuk Institute of Science and Technology) has succeeded in developing an artificial synaptic device that mimics the function of the nerve cells (neurons) and synapses that are response for memory in human brains. [sic]

Synapses are where axons and dendrites meet so that neurons in the human brain can send and receive nerve signals; there are known to be hundreds of trillions of synapses in the human brain.

This chemical synapse information transfer system, which transfers information from the brain, can handle high-level parallel arithmetic with very little energy, so research on artificial synaptic devices, which mimic the biological function of a synapse, is under way worldwide.

Dr. Lee’s research team, through joint research with teams led by Professor Gyeong-Su Park from Seoul National University; Professor Sung Kyu Park from Chung-ang University; and Professor Hyunsang Hwang from Pohang University of Science and Technology (POSTEC), developed a high-reliability artificial synaptic device with multiple values by structuring tantalum oxide — a trans-metallic material — into two layers of Ta2O5-x and TaO2-x and by controlling its surface.

A September 7, 2018 DGIST press release (also on EurekAlert), which originated the news item, delves further into the work,

The artificial synaptic device developed by the research team is an electrical synaptic device that simulates the function of synapses in the brain as the resistance of the tantalum oxide layer gradually increases or decreases depending on the strength of the electric signals. It has succeeded in overcoming durability limitations of current devices by allowing current control only on one layer of Ta2O5-x.

In addition, the research team successfully implemented an experiment that realized synapse plasticity [or synaptic plasticity], which is the process of creating, storing, and deleting memories, such as long-term strengthening of memory and long-term suppression of memory deleting by adjusting the strength of the synapse connection between neurons.

The non-volatile multiple-value data storage method applied by the research team has the technological advantage of having a small area of an artificial synaptic device system, reducing circuit connection complexity, and reducing power consumption by more than one-thousandth compared to data storage methods based on digital signals using 0 and 1 such as volatile CMOS (Complementary Metal Oxide Semiconductor).

The high-reliability artificial synaptic device developed by the research team can be used in ultra-low-power devices or circuits for processing massive amounts of big data due to its capability of low-power parallel arithmetic. It is expected to be applied to next-generation intelligent semiconductor device technologies such as development of artificial intelligence (AI) including machine learning and deep learning and brain-mimicking semiconductors.

Dr. Lee said, “This research secured the reliability of existing artificial synaptic devices and improved the areas pointed out as disadvantages. We expect to contribute to the development of AI based on the neuromorphic system that mimics the human brain by creating a circuit that imitates the function of neurons.”

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

Reliable Multivalued Conductance States in TaOx Memristors through Oxygen Plasma-Assisted Electrode Deposition with in Situ-Biased Conductance State Transmission Electron Microscopy Analysis by Myoung-Jae Lee, Gyeong-Su Park, David H. Seo, Sung Min Kwon, Hyeon-Jun Lee, June-Seo Kim, MinKyung Jung, Chun-Yeol You, Hyangsook Lee, Hee-Goo Kim, Su-Been Pang, Sunae Seo, Hyunsang Hwang, and Sung Kyu Park. ACS Appl. Mater. Interfaces, 2018, 10 (35), pp 29757–29765 DOI: 10.1021/acsami.8b09046 Publication Date (Web): July 23, 2018

Copyright © 2018 American Chemical Society

This paper is open access.

You can find other memristor and neuromorphic computing stories here by using the search terms I’ve highlighted,  My latest (more or less) is an April 19, 2018 posting titled, New path to viable memristor/neuristor?

Finally, here’s an image from the Korean researchers that accompanied their work,

Caption: Representation of neurons and synapses in the human brain. The magnified synapse represents the portion mimicked using solid-state devices. Credit: Daegu Gyeongbuk Institute of Science and Technology(DGIST)

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’,