Tag Archives: brainlike computing

Fluidic memristor with neuromorphic (brainlike) functions

I think this is the first time I’ve had occasion to feature a fluidic memristor. From a January 13, 2023 news item on Nahowerk, Note: Links have been removed,

Neuromorphic devices have attracted increasing attention because of their potential applications in neuromorphic [brainlike] computing, intelligence sensing, brain-machine interfaces and neuroprosthetics. However, most of the neuromorphic functions realized are based on the mimic of electric pulses with solid state devices. Mimicking the functions of chemical synapses, especially neurotransmitter-related functions, is still a challenge in this research area.

In a study published in Science (“Neuromorphic functions with a polyelectrolyte-confined fluidic memristor”), the research group led by Prof. YU Ping and MAO Lanqun from the Institute of Chemistry of the Chinese Academy of Sciences developed a polyelectrolyte-confined fluidic memristor (PFM), which could emulate diverse electric pulse with ultralow energy consumption. Moreover, benefitting from the fluidic nature of PFM, chemical-regulated electric pulses and chemical-electric signal transduction could also be emulated.

A January 12, 2023 Chinese Academy of Science (CAS) press release, which originated the news item, offers more technical detail,

The researchers first fabricated the polyelectrolyte-confined fluidic channel by surface-initiated atomic transfer polymerization. By systematically studying the current-voltage relationship, they found that the fabricated fluidic channel well satisfied the nature memristor, defined as PFM. The origin of the ion memory was originated from the relatively slow diffusion dynamics of anions into and out of the polyelectrolyte brushes.  

The PFM could well emulate the short-term plasticity patterns (STP), including paired-pulse facilitation and paired-pulse depression. These functions can be operated at the voltage and energy consumption as low as those biological systems, suggesting the potential application in bioinspired sensorimotor implementation, intelligent sensing and neuroprosthetics.  

The PFM could also emulate the chemical-regulated STP electric pulses. Based on the interaction between polyelectrolyte and counterions, the retention time could be regulated in different electrolyte.

More importantly, in a physiological electrolyte (i.e., phosphate-buffered saline solution, pH7.4), the PFM could emulate the regulation of memory by adenosine triphosphate (ATP), demonstrating the possibility to regulate the synaptic plasticity by neurotransmitter.  More importantly, based on the interaction between polyelectrolytes and counterions, the chemical-electric signal transduction was accomplished with the PFM, which is a key step towards the fabrication of artificial chemical synapses.

With structural emulation to ion channels, PFM features versatility and easily interfaces with biological systems, paving a way to building neuromorphic devices with advanced functions by introducing rich chemical designs. This study provides a new way to interface the chemistry with neuromorphic device. 

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

Neuromorphic functions with a polyelectrolyte-confined fluidic memristor by Tianyi Xiong, Changwei Li, Xiulan He, Boyang Xie, Jianwei Zong, Yanan Jiang, Wenjie Ma, Fei Wu, Junjie Fei, Ping Yu, and Lanqun Mao. Science 12 Jan 2023 Vol 379, Issue 6628 pp. 156-161 DOI: 10.1126/science.adc9150

This paper is behind a paywall.

Analogue memristor for next-generation brain-mimicking (neuromorphic) computing

This research into an analogue memristor comes from The Korea Institute of Science and Technology (KIST) according to a September 20, 2022 news item on Nanowerk, Note: A link has been removed,

Neuromorphic computing system technology mimicking the human brain has emerged and overcome the limitation of excessive power consumption regarding the existing von Neumann computing method. A high-performance, analog artificial synapse device, capable of expressing various synapse connection strengths, is required to implement a semiconductor device that uses a brain information transmission method. This method uses signals transmitted between neurons when a neuron generates a spike signal.

However, considering conventional resistance-variable memory devices widely used as artificial synapses, as the filament grows with varying resistance, the electric field increases, causing a feedback phenomenon, resulting in rapid filament growth. Therefore, it is challenging to implement considerable plasticity while maintaining analog (gradual) resistance variation concerning the filament type.

The Korea Institute of Science and Technology (KIST), led by Dr. YeonJoo Jeong’s team at the Center for Neuromorphic Engineering, solved the limitations of analog synaptic characteristics, plasticity and information preservation, which are chronic obstacles regarding memristors, neuromorphic semiconductor devices. He announced the development of an artificial synaptic semiconductor device capable of highly reliable neuromorphic computing (Nature Communications, “Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing”).

Caption: Concept image of the article Credit: Korea Institute of Science and Technology (KIST)

A September 20, 2022 (Korea) National Research Council of Science & Technology press release on EurekAlert, which originated the news item, delves further into the research,

The KIST research team fine-tuned the redox properties of active electrode ions to solve small synaptic plasticity hindering the performance of existing neuromorphic semiconductor devices. Furthermore, various transition metals were doped and used in the synaptic device, controlling the reduction probability of active electrode ions. It was discovered that the high reduction probability of ions is a critical variable in the development of high-performance artificial synaptic devices.

Therefore, a titanium transition metal, having a high ion reduction probability, was introduced by the research team into an existing artificial synaptic device. This maintains the synapse’s analog characteristics and the device plasticity at the synapse of the biological brain, approximately five times the difference between high and low resistances. Furthermore, they developed a high-performance neuromorphic semiconductor that is approximately 50 times more efficient.

Additionally, due to the high alloy formation reaction concerning the doped titanium transition metal, the information retention increased up to 63 times compared with the existing artificial synaptic device. Furthermore, brain functions, including long-term potentiation and long-term depression, could be more precisely simulated.

The team implemented an artificial neural network learning pattern using the developed artificial synaptic device and attempted artificial intelligence image recognition learning. As a result, the error rate was reduced by more than 60% compared with the existing artificial synaptic device; additionally, the handwriting image pattern (MNIST) recognition accuracy increased by more than 69%. The research team confirmed the feasibility of a high-performance neuromorphic computing system through this improved the artificial synaptic device.

Dr. Jeong of KIST stated, “This study drastically improved the synaptic range of motion and information preservation, which were the greatest technical barriers of existing synaptic mimics.” “In the developed artificial synapse device, the device’s analog operation area to express the synapse’s various connection strengths has been maximized, so the performance of brain simulation-based artificial intelligence computing will be improved.” Additionally, he mentioned, “In the follow-up research, we will manufacture a neuromorphic semiconductor chip based on the developed artificial synapse device to realize a high-performance artificial intelligence system, thereby further enhancing competitiveness in the domestic system and artificial intelligence semiconductor field.”

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

Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing by Jaehyun Kang, Taeyoon Kim, Suman Hu, Jaewook Kim, Joon Young Kwak, Jongkil Park, Jong Keuk Park, Inho Kim, Suyoun Lee, Sangbum Kim & YeonJoo Jeong. Nature Communications volume 13, Article number: 4040 (2022) DOI: https://doi.org/10.1038/s41467-022-31804-4 Published: 12 July 2022

This paper is open access.

Dynamic molecular switches for brainlike computing at the University of Limerick

Aren’t memristors proof that brainlike computing at the molecular and atomic levels is possible? It seems I have misunderstood memristors according to this November 21, 2022 news item on ScienceDaily,

A breakthrough discovery at University of Limerick in Ireland has revealed for the first time that unconventional brain-like computing at the tiniest scale of atoms and molecules is possible.

Researchers at University of Limerick’s Bernal Institute worked with an international team of scientists to create a new type of organic material that learns from its past behaviour.

The discovery of the ‘dynamic molecular switch’ that emulate[s] synaptic behaviour is revealed in a new study in the international journal Nature Materials.

The study was led by Damien Thompson, Professor of Molecular Modelling in UL’s Department of Physics and Director of SSPC, the UL-hosted Science Foundation Ireland Research Centre for Pharmaceuticals, together with Christian Nijhuis at the Centre for Molecules and Brain-Inspired Nano Systems in University of Twente [Netherlands] and Enrique del Barco from University of Central Florida.

A November 21, 2022 University of Limerick press release (also on EurekAlert), which originated the news item, provides more technical details about the research,

Working during lockdowns, the team developed a two-nanometre thick layer of molecules, which is 50,000 times thinner than a strand of hair and remembers its history as electrons pass through it.

Professor Thompson explained that the “switching probability and the values of the on/off states continually change in the molecular material, which provides a disruptive new alternative to conventional silicon-based digital switches that can only ever be either on or off”.

The newly discovered dynamic organic switch displays all the mathematical logic functions necessary for deep learning, successfully emulating Pavlovian ‘call and response’ synaptic brain-like behaviour.

The researchers demonstrated the new materials properties using extensive experimental characterisation and electrical measurements supported by multi-scale modelling spanning from predictive modelling of the molecular structures at the quantum level to analytical mathematical modelling of the electrical data.

To emulate the dynamical behaviour of synapses at the molecular level, the researchers combined fast electron transfer (akin to action potentials and fast depolarization processes in biology) with slow proton coupling limited by diffusion (akin to the role of biological calcium ions or neurotransmitters).

Since the electron transfer and proton coupling steps inside the material occur at very different time scales, the transformation can emulate the plastic behaviour of synapse neuronal junctions, Pavlovian learning, and all logic gates for digital circuits, simply by changing the applied voltage and the duration of voltage pulses during the synthesis, they explained.

“This was a great lockdown project, with Chris, Enrique and I pushing each other through zoom meetings and gargantuan email threads to bring our teams combined skills in materials modelling, synthesis and characterisation to the point where we could demonstrate these new brain-like computing properties,” explained Professor Thompson.

“The community has long known that silicon technology works completely differently to how our brains work and so we used new types of electronic materials based on soft molecules to emulate brain-like computing networks.”

The researchers explained that the method can in the future be applied to dynamic molecular systems driven by other stimuli such as light and coupled to different types of dynamic covalent bond formation.

This breakthrough opens up a whole new range of adaptive and reconfigurable systems, creating new opportunities in sustainable and green chemistry, from more efficient flow chemistry production of drug products and other value-added chemicals to development of new organic materials for high density computing and memory storage in big data centres.

“This is just the start. We are already busy expanding this next generation of intelligent molecular materials, which is enabling development of sustainable alternative technologies to tackle grand challenges in energy, environment, and health,” explained Professor Thompson.

Professor Norelee Kennedy, Vice President Research at UL, said: “Our researchers are continuously finding new ways of making more effective, more sustainable materials. This latest finding is very exciting, demonstrating the reach and ambition of our international collaborations and showcasing our world-leading ability at UL to encode useful properties into organic materials.”

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

Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour by Yulong Wang, Qian Zhang, Hippolyte P. A. G. Astier, Cameron Nickle, Saurabh Soni, Fuad A. Alami, Alessandro Borrini, Ziyu Zhang, Christian Honnigfort, Björn Braunschweig, Andrea Leoncini, Dong-Cheng Qi, Yingmei Han, Enrique del Barco, Damien Thompson & Christian A. Nijhuis. Nature Materials volume 21, pages 1403–1411 (2022) DOI: https://doi.org/10.1038/s41563-022-01402-2 Published: 21 November 2022 Issue Date: December 2022

This paper is behind a paywall.

Sleep helps artificial neural networks (ANNs) to keep learning without “catastrophic forgetting”

A November 18, 2022 news item on phys.org describes some of the latest work on neuromorphic (brainlike) computing from the University of California at San Diego (UCSD or UC San Diego), Note: Links have been removed,

Depending on age, humans need 7 to 13 hours of sleep per 24 hours. During this time, a lot happens: Heart rate, breathing and metabolism ebb and flow; hormone levels adjust; the body relaxes. Not so much in the brain.

“The brain is very busy when we sleep, repeating what we have learned during the day,” said Maxim Bazhenov, Ph.D., professor of medicine and a sleep researcher at University of California San Diego School of Medicine. “Sleep helps reorganize memories and presents them in the most efficient way.”

In previous published work, Bazhenov and colleagues have reported how sleep builds rational memory, the ability to remember arbitrary or indirect associations between objects, people or events, and protects against forgetting old memories.

Artificial neural networks leverage the architecture of the human brain to improve numerous technologies and systems, from basic science and medicine to finance and social media. In some ways, they have achieved superhuman performance, such as computational speed, but they fail in one key aspect: When artificial neural networks learn sequentially, new information overwrites previous information, a phenomenon called catastrophic forgetting.

“In contrast, the human brain learns continuously and incorporates new data into existing knowledge,” said Bazhenov, “and it typically learns best when new training is interleaved with periods of sleep for memory consolidation.”

Writing in the November 18, 2022 issue of PLOS Computational Biology, senior author Bazhenov and colleagues discuss how biological models may help mitigate the threat of catastrophic forgetting in artificial neural networks, boosting their utility across a spectrum of research interests. 

A November 18, 2022 UC San Diego news release (also one EurekAlert), which originated the news item, adds some technical details,

The scientists used spiking neural networks that artificially mimic natural neural systems: Instead of information being communicated continuously, it is transmitted as discrete events (spikes) at certain time points.

They found that when the spiking networks were trained on a new task, but with occasional off-line periods that mimicked sleep, catastrophic forgetting was mitigated. Like the human brain, said the study authors, “sleep” for the networks allowed them to replay old memories without explicitly using old training data. 

Memories are represented in the human brain by patterns of synaptic weight — the strength or amplitude of a connection between two neurons. 

“When we learn new information,” said Bazhenov, “neurons fire in specific order and this increases synapses between them. During sleep, the spiking patterns learned during our awake state are repeated spontaneously. It’s called reactivation or replay. 

“Synaptic plasticity, the capacity to be altered or molded, is still in place during sleep and it can further enhance synaptic weight patterns that represent the memory, helping to prevent forgetting or to enable transfer of knowledge from old to new tasks.”

When Bazhenov and colleagues applied this approach to artificial neural networks, they found that it helped the networks avoid catastrophic forgetting. 

“It meant that these networks could learn continuously, like humans or animals. Understanding how human brain processes information during sleep can help to augment memory in human subjects. Augmenting sleep rhythms can lead to better memory. 

“In other projects, we use computer models to develop optimal strategies to apply stimulation during sleep, such as auditory tones, that enhance sleep rhythms and improve learning. This may be particularly important when memory is non-optimal, such as when memory declines in aging or in some conditions like Alzheimer’s disease.”

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

Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation by Ryan Golden, Jean Erik Delanois, Pavel Sanda, Maxim Bazhenov. PLOS [Computational Biology] DOI: https://doi.org/10.1371/journal.pcbi.1010628 Published: November 18, 2022

This paper is open access.

Studying quantum conductance in memristive devices

A September 27, 2022 news item on phys.org provides an introduction to the later discussion of quantum effects in memristors,

At the nanoscale, the laws of classical physics suddenly become inadequate to explain the behavior of matter. It is precisely at this juncture that quantum theory comes into play, effectively describing the physical phenomena characteristic of the atomic and subatomic world. Thanks to the different behavior of matter on these length and energy scales, it is possible to develop new materials, devices and technologies based on quantum effects, which could yield a real quantum revolution that promises to innovate areas such as cryptography, telecommunications and computation.

The physics of very small objects, already at the basis of many technologies that we use today, is intrinsically linked to the world of nanotechnologies, the branch of applied science dealing with the control of matter at the nanometer scale (a nanometer is one billionth of a meter). This control of matter at the nanoscale is at the basis of the development of new electronic devices.

A September 27, 2022 Istituto Nazionale di Ricerca Metrologica (INRIM) press release (summary, PDF, and also on EurekAlert), which originated the news item, provides more information about the research,

Among these, memrisistors are considered promising devices for the realization of new computational architectures emulating functions of our brain, allowing the creation of increasingly efficient computation systems suitable for the development of the entire artificial intelligence sector, as recently shown by INRiM researchers in collaboration with several international universities and research institutes [1,2].

In this context, the EMPIR MEMQuD project, coordinated by INRiM, aims to study the quantum effects in such devices in which the electronic conduction properties can be manipulated allowing the observation of quantized conductivity phenomena at room temperature. In addition to analyzing the fundamentals and recent developments, the review work “Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications” recently published in the prestigious international journal Advanced Materials (https://doi.org/10.1002/adma.202201248) analyzes how these effects can be used for a wide range of applications, from metrology to the development of next-generation memories and artificial intelligence.

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

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications by Gianluca Milano, Masakazu Aono, Luca Boarino, Umberto Celano, Tsuyoshi Hasegawa, Michael Kozicki, Sayani Majumdar, Mariela Menghini, Enrique Miranda, Carlo Ricciardi, Stefan Tappertzhofen, Kazuya Terabe, Ilia Valov. Advanced Materials Volume 34, Issue32 August 11, 2022 2201248 DOI: https://doi.org/10.1002/adma.202201248 First published: 11 April 2022

This paper is open access.

You can find the EMPIR (European Metrology Programme for Innovation and Research) MEMQuD (quantum effects in memristive devices) project here, from the homepage,

Memristive devices are electrical resistance switches that couple ionics (i.e. dynamics of ions) with electronics. These devices offer a promising platform to observe quantum effects in air, at room temperature, and without an applied magnetic field. For this reason, they can be traced to fundamental physics constants fixed in the revised International System of Units (SI) for the realization of a quantum-based standard of resistance. However, as an emerging technology, memristive devices lack standardization and insights into the fundamental physics underlying its working principles.

The overall aim of the project is to investigate and exploit quantized conductance effects in memristive devices that operate reliably, in air and at room temperature. In particular, the project will focus on the development of memristive model systems and nanometrological characterization techniques at the nanoscale level of memristive devices, in order to better understand and control the quantized effects in memristive devices. Such an outcome would enable not only the development of neuromorphic systems but also the realization of a standard of resistance implementable on-chip for self-calibrating systems with zero-chain traceability in the spirit of the revised SI.

I’m starting to see mention of ‘neuromorphic computing’ in advertisements (specifically a Mercedes Benz car). I will have more about these first mentions of neuromorphic computing in consumer products in a future posting.

Synaptic transistors for brainlike computers based on (more environmentally friendly) graphene

An August 9, 2022 news item on ScienceDaily describes research investigating materials other than silicon for neuromorphic (brainlike) computing purposes,

Computers that think more like human brains are inching closer to mainstream adoption. But many unanswered questions remain. Among the most pressing, what types of materials can serve as the best building blocks to unlock the potential of this new style of computing.

For most traditional computing devices, silicon remains the gold standard. However, there is a movement to use more flexible, efficient and environmentally friendly materials for these brain-like devices.

In a new paper, researchers from The University of Texas at Austin developed synaptic transistors for brain-like computers using the thin, flexible material graphene. These transistors are similar to synapses in the brain, that connect neurons to each other.

An August 8, 2022 University of Texas at Austin news release (also on EurekAlert but published August 9, 2022), which originated the news item, provides more detail about the research,

“Computers that think like brains can do so much more than today’s devices,” said Jean Anne Incorvia, an assistant professor in the Cockrell School of Engineering’s Department of Electrical and Computer Engineer and the lead author on the paper published today in Nature Communications. “And by mimicking synapses, we can teach these devices to learn on the fly, without requiring huge training methods that take up so much power.”

The Research: A combination of graphene and nafion, a polymer membrane material, make up the backbone of the synaptic transistor. Together, these materials demonstrate key synaptic-like behaviors — most importantly, the ability for the pathways to strengthen over time as they are used more often, a type of neural muscle memory. In computing, this means that devices will be able to get better at tasks like recognizing and interpreting images over time and do it faster.

Another important finding is that these transistors are biocompatible, which means they can interact with living cells and tissue. That is key for potential applications in medical devices that come into contact with the human body. Most materials used for these early brain-like devices are toxic, so they would not be able to contact living cells in any way.

Why It Matters: With new high-tech concepts like self-driving cars, drones and robots, we are reaching the limits of what silicon chips can efficiently do in terms of data processing and storage. For these next-generation technologies, a new computing paradigm is needed. Neuromorphic devices mimic processing capabilities of the brain, a powerful computer for immersive tasks.

“Biocompatibility, flexibility, and softness of our artificial synapses is essential,” said Dmitry Kireev, a post-doctoral researcher who co-led the project. “In the future, we envision their direct integration with the human brain, paving the way for futuristic brain prosthesis.”

Will It Really Happen: Neuromorphic platforms are starting to become more common. Leading chipmakers such as Intel and Samsung have either produced neuromorphic chips already or are in the process of developing them. However, current chip materials place limitations on what neuromorphic devices can do, so academic researchers are working hard to find the perfect materials for soft brain-like computers.

“It’s still a big open space when it comes to materials; it hasn’t been narrowed down to the next big solution to try,” Incorvia said. “And it might not be narrowed down to just one solution, with different materials making more sense for different applications.”

The Team: The research was led by Incorvia and Deji Akinwande, professor in the Department of Electrical and Computer Engineering. The two have collaborated many times together in the past, and Akinwande is a leading expert in graphene, using it in multiple research breakthroughs, most recently as part of a wearable electronic tattoo for blood pressure monitoring.

The idea for the project was conceived by Samuel Liu, a Ph.D. student and first author on the paper, in a class taught by Akinwande. Kireev then suggested the specific project. Harrison Jin, an undergraduate electrical and computer engineering student, measured the devices and analyzed data.

The team collaborated with T. Patrick Xiao and Christopher Bennett of Sandia National Laboratories, who ran neural network simulations and analyzed the resulting data.

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

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing by Dmitry Kireev, Samuel Liu, Harrison Jin, T. Patrick Xiao, Christopher H. Bennett, Deji Akinwande & Jean Anne C. Incorvia. Nature Communications volume 13, Article number: 4386 (2022) DOI: https://doi.org/10.1038/s41467-022-32078-6 Published: 28 July 2022

This paper is open access.

Neuromorphic computing and liquid-light interaction

Simulation result of light affecting liquid geometry, which in turn affects reflection and transmission properties of the optical mode, thus constituting a two-way light–liquid interaction mechanism. The degree of deformation serves as an optical memory allowing to store the power magnitude of the previous optical pulse and use fluid dynamics to affect the subsequent optical pulse at the same actuation region, thus constituting an architecture where memory is part of the computation process. Credit: Gao et al., doi 10.1117/1.AP.4.4.046005

This is a fascinating approach to neuromorphic (brainlike) computing and given my recent post (August 29, 2022) about human cells being incorporated into computer chips, it’s part o my recent spate of posts about neuromorphic computing. From a July 25, 2022 news item on phys.org,

Sunlight sparkling on water evokes the rich phenomena of liquid-light interaction, spanning spatial and temporal scales. While the dynamics of liquids have fascinated researchers for decades, the rise of neuromorphic computing has sparked significant efforts to develop new, unconventional computational schemes based on recurrent neural networks, crucial to supporting wide range of modern technological applications, such as pattern recognition and autonomous driving. As biological neurons also rely on a liquid environment, a convergence may be attained by bringing nanoscale nonlinear fluid dynamics to neuromorphic computing.

A July 25, 2022 SPIE (International Society for Optics and Photonics) press release (also on EurekAlert), which originated the news item,

Researchers from University of California San Diego recently proposed a novel paradigm where liquids, which usually do not strongly interact with light on a micro- or nanoscale, support significant nonlinear response to optical fields. As reported in Advanced Photonics, the researchers predict a substantial light–liquid interaction effect through a proposed nanoscale gold patch operating as an optical heater and generating thickness changes in a liquid film covering the waveguide.

The liquid film functions as an optical memory. Here’s how it works: Light in the waveguide affects the geometry of the liquid surface, while changes in the shape of the liquid surface affect the properties of the optical mode in the waveguide, thus constituting a mutual coupling between the optical mode and the liquid film. Importantly, as the liquid geometry changes, the properties of the optical mode undergo a nonlinear response; after the optical pulse stops, the magnitude of liquid film’s deformation indicates the power of the previous optical pulse.

Remarkably, unlike traditional computational approaches, the nonlinear response and the memory reside at the same spatial region, thus suggesting realization of a compact (beyond von-Neumann) architecture where memory and computational unit occupy the same space. The researchers demonstrate that the combination of memory and nonlinearity allow the possibility of “reservoir computing” capable of performing digital and analog tasks, such as nonlinear logic gates and handwritten image recognition.

Their model also exploits another significant liquid feature: nonlocality. This enables them to predict computation enhancement that is simply not possible in solid state material platforms with limited nonlocal spatial scale. Despite nonlocality, the model does not quite achieve the levels of modern solid-state optics-based reservoir computing systems, yet the work nonetheless presents a clear roadmap for future experimental works aiming to validate the predicted effects and explore intricate coupling mechanisms of various physical processes in a liquid environment for computation.

Using multiphysics simulations to investigate coupling between light, fluid dynamics, heat transport, and surface tension effects, the researchers predict a family of novel nonlinear and nonlocal optical effects. They go a step further by indicating how these can be used to realize versatile, nonconventional computational platforms. Taking advantage of a mature silicon photonics platform, they suggest improvements to state-of-the-art liquid-assisted computation platforms by around five orders magnitude in space and at least two orders of magnitude in speed.

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

Thin liquid film as an optical nonlinear-nonlocal medium and memory element in integrated optofluidic reservoir computer by Chengkuan Gao, Prabhav Gaur, Shimon Rubin, Yeshaiahu Fainman. Advanced Photonics, 4(4), 046005 (2022). https://doi.org/10.1117/1.AP.4.4.046005 Published: 1 July 2022

This paper is open access.

Guide for memristive hardware design

An August 15 ,2022 news item on ScienceDaily announces a type of guide for memristive hardware design,

They are many times faster than flash memory and require significantly less energy: memristive memory cells could revolutionize the energy efficiency of neuromorphic [brainlike] computers. In these computers, which are modeled on the way the human brain works, memristive cells function like artificial synapses. Numerous groups around the world are working on the use of corresponding neuromorphic circuits — but often with a lack of understanding of how they work and with faulty models. Jülich researchers have now summarized the physical principles and models in a comprehensive review article in the renowned journal Advances in Physics.

An August 15, 2022 Forschungszentrum Juelich press release (also on EurekAlert), which originated the news item, describes two papers designed to help researchers better understand and design memristive hardware,

Certain tasks – such as recognizing patterns and language – are performed highly efficiently by a human brain, requiring only about one ten-thousandth of the energy of a conventional, so-called “von Neumann” computer. One of the reasons lies in the structural differences: In a von Neumann architecture, there is a clear separation between memory and processor, which requires constant moving of large amounts of data. This is time and energy consuming – the so-called von Neumann bottleneck. In the brain, the computational operation takes place directly in the data memory and the biological synapses perform the tasks of memory and processor at the same time.

In Jülich, scientists have been working for more than 15 years on special data storage devices and components that can have similar properties to the synapses in the human brain. So-called memristive memory devices, also known as memristors, are considered to be extremely fast, energy-saving and can be miniaturized very well down to the nanometer range. The functioning of memristive cells is based on a very special effect: Their electrical resistance is not constant, but can be changed and reset again by applying an external voltage, theoretically continuously. The change in resistance is controlled by the movement of oxygen ions. If these move out of the semiconducting metal oxide layer, the material becomes more conductive and the electrical resistance drops. This change in resistance can be used to store information.

The processes that can occur in cells are very complex and vary depending on the material system. Three researchers from the Jülich Peter Grünberg Institute – Prof. Regina Dittmann, Dr. Stephan Menzel, and Prof. Rainer Waser – have therefore compiled their research results in a detailed review article, “Nanoionic memristive phenomena in metal oxides: the valence change mechanism”. They explain in detail the various physical and chemical effects in memristors and shed light on the influence of these effects on the switching properties of memristive cells and their reliability.

“If you look at current research activities in the field of neuromorphic memristor circuits, they are often based on empirical approaches to material optimization,” said Rainer Waser, director at the Peter Grünberg Institute. “Our goal with our review article is to give researchers something to work with in order to enable insight-driven material optimization.” The team of authors worked on the approximately 200-page article for ten years and naturally had to keep incorporating advances in knowledge.

“The analogous functioning of memristive cells required for their use as artificial synapses is not the normal case. Usually, there are sudden jumps in resistance, generated by the mutual amplification of ionic motion and Joule heat,” explains Regina Dittmann of the Peter Grünberg Institute. “In our review article, we provide researchers with the necessary understanding of how to change the dynamics of the cells to enable an analog operating mode.”

“You see time and again that groups simulate their memristor circuits with models that don’t take into account high dynamics of the cells at all. These circuits will never work.” said Stephan Menzel, who leads modeling activities at the Peter Grünberg Institute and has developed powerful compact models that are now in the public domain (www.emrl.de/jart.html). “In our review article, we provide the basics that are extremely helpful for a correct use of our compact models.”

Roadmap neuromorphic computing

The “Roadmap of Neuromorphic Computing and Engineering”, which was published in May 2022, shows how neuromorphic computing can help to reduce the enormous energy consumption of IT globally. In it, researchers from the Peter Grünberg Institute (PGI-7), together with leading experts in the field, have compiled the various technological possibilities, computational approaches, learning algorithms and fields of application. 

According to the study, applications in the field of artificial intelligence, such as pattern recognition or speech recognition, are likely to benefit in a special way from the use of neuromorphic hardware. This is because they are based – much more so than classical numerical computing operations – on the shifting of large amounts of data. Memristive cells make it possible to process these gigantic data sets directly in memory without transporting them back and forth between processor and memory. This could reduce the energy efficiency of artificial neural networks by orders of magnitude.

Memristive cells can also be interconnected to form high-density matrices that enable neural networks to learn locally. This so-called edge computing thus shifts computations from the data center to the factory floor, the vehicle, or the home of people in need of care. Thus, monitoring and controlling processes or initiating rescue measures can be done without sending data via a cloud. “This achieves two things at the same time: you save energy, and at the same time, personal data and data relevant to security remain on site,” says Prof. Dittmann, who played a key role in creating the roadmap as editor.

Here’s a link to and a citation for the ‘roadmap’,

2022 roadmap on neuromorphic computing and engineering by Dennis V Christensen, Regina Dittmann, Bernabe Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J Quill, Scott T Keene, Alberto Salleo, Julie Grollier, Danijela Marković, Alice Mizrahi, Peng Yao, J Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H P Pernice, Harish Bhaskaran, Steve Furber, Emre Neftci, Franz Scherr, Wolfgang Maass, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A Cleland, Christoph Posch, ShihChii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini and N Pryds. Neuromorphic Computing and Engineering , Volume 2, Number 2 DOI: 10.1088/2634-4386/ac4a83 20 May 2022 • © 2022 The Author(s)

This paper is open access.

Here’s the most recent paper,

Nanoionic memristive phenomena in metal oxides: the valence change mechanism by Regina Dittmann, Stephan Menzel & Rainer Waser. Advances in Physics
Volume 70, 2021 – Issue 2 Pages 155-349 DOI: https://doi.org/10.1080/00018732.2022.2084006 Published online: 06 Aug 2022

This paper is behind a paywall.

FrogHeart’s 2022 comes to an end as 2023 comes into view

I look forward to 2023 and hope it will be as stimulating as 2022 proved to be. Here’s an overview of the year that was on this blog:

Sounds of science

It seems 2022 was the year that science discovered the importance of sound and the possibilities of data sonification. Neither is new but this year seemed to signal a surge of interest or maybe I just happened to stumble onto more of the stories than usual.

This is not an exhaustive list, you can check out my ‘Music’ category for more here. I have tried to include audio files with the postings but it all depends on how accessible the researchers have made them.

Aliens on earth: machinic biology and/or biological machinery?

When I first started following stories in 2008 (?) about technology or machinery being integrated with the human body, it was mostly about assistive technologies such as neuroprosthetics. You’ll find most of this year’s material in the ‘Human Enhancement’ category or you can search the tag ‘machine/flesh’.

However, the line between biology and machine became a bit more blurry for me this year. You can see what’s happening in the titles listed below (you may recognize the zenobot story; there was an earlier version of xenobots featured here in 2021):

This was the story that shook me,

Are the aliens going to come from outer space or are we becoming the aliens?

Brains (biological and otherwise), AI, & our latest age of anxiety

As we integrate machines into our bodies, including our brains, there are new issues to consider:

  • Going blind when your neural implant company flirts with bankruptcy (long read) April 5, 2022 posting
  • US National Academies Sept. 22-23, 2022 workshop on techno, legal & ethical issues of brain-machine interfaces (BMIs) September 21, 2022 posting

I hope the US National Academies issues a report on their “Brain-Machine and Related Neural Interface Technologies: Scientific, Technical, Ethical, and Regulatory Issues – A Workshop” for 2023.

Meanwhile the race to create brainlike computers continues and I have a number of posts which can be found under the category of ‘neuromorphic engineering’ or you can use these search terms ‘brainlike computing’ and ‘memristors’.

On the artificial intelligence (AI) side of things, I finally broke down and added an ‘artificial intelligence (AI) category to this blog sometime between May and August 2021. Previously, I had used the ‘robots’ category as a catchall. There are other stories but these ones feature public engagement and policy (btw, it’s a Canadian Science Policy Centre event), respectively,

  • “The “We are AI” series gives citizens a primer on AI” March 23, 2022 posting
  • “Age of AI and Big Data – Impact on Justice, Human Rights and Privacy Zoom event on September 28, 2022 at 12 – 1:30 pm EDT” September 16, 2022 posting

These stories feature problems, which aren’t new but seem to be getting more attention,

While there have been issues over AI, the arts, and creativity previously, this year they sprang into high relief. The list starts with my two-part review of the Vancouver Art Gallery’s AI show; I share most of my concerns in part two. The third post covers intellectual property issues (mostly visual arts but literary arts get a nod too). The fourth post upends the discussion,

  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (1 of 2): The Objects” July 28, 2022 posting
  • “Mad, bad, and dangerous to know? Artificial Intelligence at the Vancouver (Canada) Art Gallery (2 of 2): Meditations” July 28, 2022 posting
  • “AI (artificial intelligence) and art ethics: a debate + a Botto (AI artist) October 2022 exhibition in the Uk” October 24, 2022 posting
  • Should AI algorithms get patents for their inventions and is anyone talking about copyright for texts written by AI algorithms? August 30, 2022 posting

Interestingly, most of the concerns seem to be coming from the visual and literary arts communities; I haven’t come across major concerns from the music community. (The curious can check out Vancouver’s Metacreation Lab for Artificial Intelligence [located on a Simon Fraser University campus]. I haven’t seen any cautionary or warning essays there; it’s run by an AI and creativity enthusiast [professor Philippe Pasquier]. The dominant but not sole focus is art, i.e., music and AI.)

There is a ‘new kid on the block’ which has been attracting a lot of attention this month. If you’re curious about the latest and greatest AI anxiety,

  • Peter Csathy’s December 21, 2022 Yahoo News article (originally published in The WRAP) makes this proclamation in the headline “Chat GPT Proves That AI Could Be a Major Threat to Hollywood Creatives – and Not Just Below the Line | PRO Insight”
  • Mouhamad Rachini’s December 15, 2022 article for the Canadian Broadcasting Corporation’s (CBC) online news overs a more generalized overview of the ‘new kid’ along with an embedded CBC Radio file which runs approximately 19 mins. 30 secs. It’s titled “ChatGPT a ‘landmark event’ for AI, but what does it mean for the future of human labour and disinformation?” The chat bot’s developer, OpenAI, has been mentioned here many times including the previously listed July 28, 2022 posting (part two of the VAG review) and the October 24, 2022 posting.

Opposite world (quantum physics in Canada)

Quantum computing made more of an impact here (my blog) than usual. it started in 2021 with the announcement of a National Quantum Strategy in the Canadian federal government budget for that year and gained some momentum in 2022:

  • “Quantum Mechanics & Gravity conference (August 15 – 19, 2022) launches Vancouver (Canada)-based Quantum Gravity Institute and more” July 26, 2022 posting Note: This turned into one of my ‘in depth’ pieces where I comment on the ‘Canadian quantum scene’ and highlight the appointment of an expert panel for the Council of Canada Academies’ report on Quantum Technologies.
  • “Bank of Canada and Multiverse Computing model complex networks & cryptocurrencies with quantum computing” July 25, 2022 posting
  • “Canada, quantum technology, and a public relations campaign?” December 29, 2022 posting

This one was a bit of a puzzle with regard to placement in this end-of-year review, it’s quantum but it’s also about brainlike computing

It’s getting hot in here

Fusion energy made some news this year.

There’s a Vancouver area company, General Fusion, highlighted in both postings and the October posting includes an embedded video of Canadian-born rapper Baba Brinkman’s “You Must LENR” [L ow E nergy N uclear R eactions or sometimes L attice E nabled N anoscale R eactions or Cold Fusion or CANR (C hemically A ssisted N uclear R eactions)].

BTW, fusion energy can generate temperatures up to 150 million degrees Celsius.

Ukraine, science, war, and unintended consequences

Here’s what you might expect,

These are the unintended consequences (from Rachel Kyte’s, Dean of the Fletcher School, Tufts University, December 26, 2022 essay on The Conversation [h/t December 27, 2022 news item on phys.org]), Note: Links have been removed,

Russian President Vladimir Putin’s war on Ukraine has reverberated through Europe and spread to other countries that have long been dependent on the region for natural gas. But while oil-producing countries and gas lobbyists are arguing for more drilling, global energy investments reflect a quickening transition to cleaner energy. [emphasis mine]

Call it the Putin effect – Russia’s war is speeding up the global shift away from fossil fuels.

In December [2022?], the International Energy Agency [IEA] published two important reports that point to the future of renewable energy.

First, the IEA revised its projection of renewable energy growth upward by 30%. It now expects the world to install as much solar and wind power in the next five years as it installed in the past 50 years.

The second report showed that energy use is becoming more efficient globally, with efficiency increasing by about 2% per year. As energy analyst Kingsmill Bond at the energy research group RMI noted, the two reports together suggest that fossil fuel demand may have peaked. While some low-income countries have been eager for deals to tap their fossil fuel resources, the IEA warns that new fossil fuel production risks becoming stranded, or uneconomic, in the next 20 years.

Kyte’s essay is not all ‘sweetness and light’ but it does provide a little optimism.

Kudos, nanotechnology, culture (pop & otherwise), fun, and a farewell in 2022

This one was a surprise for me,

Sometimes I like to know where the money comes from and I was delighted to learn of the Ărramăt Project funded through the federal government’s New Frontiers in Research Fund (NFRF). Here’s more about the Ărramăt Project from the February 14, 2022 posting,

“The Ărramăt Project is about respecting the inherent dignity and interconnectedness of peoples and Mother Earth, life and livelihood, identity and expression, biodiversity and sustainability, and stewardship and well-being. Arramăt is a word from the Tamasheq language spoken by the Tuareg people of the Sahel and Sahara regions which reflects this holistic worldview.” (Mariam Wallet Aboubakrine)

Over 150 Indigenous organizations, universities, and other partners will work together to highlight the complex problems of biodiversity loss and its implications for health and well-being. The project Team will take a broad approach and be inclusive of many different worldviews and methods for research (i.e., intersectionality, interdisciplinary, transdisciplinary). Activities will occur in 70 different kinds of ecosystems that are also spiritually, culturally, and economically important to Indigenous Peoples.

The project is led by Indigenous scholars and activists …

Kudos to the federal government and all those involved in the Salmon science camps, the Ărramăt Project, and other NFRF projects.

There are many other nanotechnology posts here but this appeals to my need for something lighter at this point,

  • “Say goodbye to crunchy (ice crystal-laden) in ice cream thanks to cellulose nanocrystals (CNC)” August 22, 2022 posting

The following posts tend to be culture-related, high and/or low but always with a science/nanotechnology edge,

Sadly, it looks like 2022 is the last year that Ada Lovelace Day is to be celebrated.

… this year’s Ada Lovelace Day is the final such event due to lack of financial backing. Suw Charman-Anderson told the BBC [British Broadcasting Corporation] the reason it was now coming to an end was:

You can read more about it here:

In the rearview mirror

A few things that didn’t fit under the previous heads but stood out for me this year. Science podcasts, which were a big feature in 2021, also proliferated in 2022. I think they might have peaked and now (in 2023) we’ll see what survives.

Nanotechnology, the main subject on this blog, continues to be investigated and increasingly integrated into products. You can search the ‘nanotechnology’ category here for posts of interest something I just tried. It surprises even me (I should know better) how broadly nanotechnology is researched and applied.

If you want a nice tidy list, Hamish Johnston in a December 29, 2022 posting on the Physics World Materials blog has this “Materials and nanotechnology: our favourite research in 2022,” Note: Links have been removed,

“Inherited nanobionics” makes its debut

The integration of nanomaterials with living organisms is a hot topic, which is why this research on “inherited nanobionics” is on our list. Ardemis Boghossian at EPFL [École polytechnique fédérale de Lausanne] in Switzerland and colleagues have shown that certain bacteria will take up single-walled carbon nanotubes (SWCNTs). What is more, when the bacteria cells split, the SWCNTs are distributed amongst the daughter cells. The team also found that bacteria containing SWCNTs produce a significantly more electricity when illuminated with light than do bacteria without nanotubes. As a result, the technique could be used to grow living solar cells, which as well as generating clean energy, also have a negative carbon footprint when it comes to manufacturing.

Getting to back to Canada, I’m finding Saskatchewan featured more prominently here. They do a good job of promoting their science, especially the folks at the Canadian Light Source (CLS), Canada’s synchrotron, in Saskatoon. Canadian live science outreach events seeming to be coming back (slowly). Cautious organizers (who have a few dollars to spare) are also enthusiastic about hybrid events which combine online and live outreach.

After what seems like a long pause, I’m stumbling across more international news, e.g. “Nigeria and its nanotechnology research” published December 19, 2022 and “China and nanotechnology” published September 6, 2022. I think there’s also an Iran piece here somewhere.

With that …

Making resolutions in the dark

Hopefully this year I will catch up with the Council of Canadian Academies (CCA) output and finally review a few of their 2021 reports such as Leaps and Boundaries; a report on artificial intelligence applied to science inquiry and, perhaps, Powering Discovery; a report on research funding and Natural Sciences and Engineering Research Council of Canada.

Given what appears to a renewed campaign to have germline editing (gene editing which affects all of your descendants) approved in Canada, I might even reach back to a late 2020 CCA report, Research to Reality; somatic gene and engineered cell therapies. it’s not the same as germline editing but gene editing exists on a continuum.

For anyone who wants to see the CCA reports for themselves they can be found here (both in progress and completed).

I’m also going to be paying more attention to how public relations and special interests influence what science is covered and how it’s covered. In doing this 2022 roundup, I noticed that I featured an overview of fusion energy not long before the breakthrough. Indirect influence on this blog?

My post was precipitated by an article by Alex Pasternak in Fast Company. I’m wondering what precipitated Alex Pasternack’s interest in fusion energy since his self-description on the Huffington Post website states this “… focus on the intersections of science, technology, media, politics, and culture. My writing about those and other topics—transportation, design, media, architecture, environment, psychology, art, music … .”

He might simply have received a press release that stimulated his imagination and/or been approached by a communications specialist or publicists with an idea. There’s a reason for why there are so many public relations/media relations jobs and agencies.

Que sera, sera (Whatever will be, will be)

I can confidently predict that 2023 has some surprises in store. I can also confidently predict that the European Union’s big research projects (1B Euros each in funding for the Graphene Flagship and Human Brain Project over a ten year period) will sunset in 2023, ten years after they were first announced in 2013. Unless, the powers that be extend the funding past 2023.

I expect the Canadian quantum community to provide more fodder for me in the form of a 2023 report on Quantum Technologies from the Council of Canadian academies, if nothing else otherwise.

I’ve already featured these 2023 science events but just in case you missed them,

  • 2023 Preview: Bill Nye the Science Guy’s live show and Marvel Avengers S.T.A.T.I.O.N. (Scientific Training And Tactical Intelligence Operative Network) coming to Vancouver (Canada) November 24, 2022 posting
  • September 2023: Auckland, Aotearoa New Zealand set to welcome women in STEM (science, technology, engineering, and mathematics) November 15, 2022 posting

Getting back to this blog, it may not seem like a new year during the first few weeks of 2023 as I have quite the stockpile of draft posts. At this point I have drafts that are dated from June 2022 and expect to be burning through them so as not to fall further behind but will be interspersing them, occasionally, with more current posts.

Most importantly: a big thank you to everyone who drops by and reads (and sometimes even comments) on my posts!!! it’s very much appreciated and on that note: I wish you all the best for 2023.

Swiss researchers, memristors, perovskite crystals, and neuromorphic (brainlike) computing

A May 18, 2022 news item on Nanowerk highlights research into making memristors more ‘flexible’, (Note: There’s an almost identical May 18, 2022 news item on ScienceDaily but the issuing agency is listed as ETH Zurich rather than Empa as listed on Nanowerk),

Compared with computers, the human brain is incredibly energy-efficient. Scientists are therefore drawing on how the brain and its interconnected neurons function for inspiration in designing innovative computing technologies. They foresee that these brain-inspired computing systems, will be more energy-efficient than conventional ones, as well as better at performing machine-learning tasks.

Much like neurons, which are responsible for both data storage and data processing in the brain, scientists want to combine storage and processing in a single type of electronic component, known as a memristor. Their hope is that this will help to achieve greater efficiency because moving data between the processor and the storage, as conventional computers do, is the main reason for the high energy consumption in machine-learning applications.

Researchers at ETH Zurich, Empa and the University of Zurich have now developed an innovative concept for a memristor that can be used in a far wider range of applications than existing memristors.

“There are different operation modes for memristors, and it is advantageous to be able to use all these modes depending on an artificial neural network’s architecture,” explains ETH Zurich postdoc Rohit John. “But previous conventional memristors had to be configured for one of these modes in advance.”

The new memristors can now easily switch between two operation modes while in use: a mode in which the signal grows weaker over time and dies (volatile mode), and one in which the signal remains constant (non-volatile mode).

Once you get past the first two paragraphs in the Nanowerk news item, you find the ETH Zurich and Empa May 18, 2022 press releases by Fabio Begamin, in both cases, are identical (ETH is listed as the authoring agency on EurekAlert), (Note: A link has been removed in the following),

Just like in the brain

“These two operation modes are also found in the human brain,” John says. On the one hand, stimuli at the synapses are transmitted from neuron to neuron with biochemical neurotransmitters. These stimuli start out strong and then gradually become weaker. On the other hand, new synaptic connections to other neurons form in the brain while we learn. These connections are longer-​lasting.

John, who is a postdoc in the group headed by ETH Professor Maksym Kovalenko, was awarded an ETH fellowship for outstanding postdoctoral researchers in 2020. John conducted this research together with Yiğit Demirağ, a doctoral student in Professor Giacomo Indiveri’s group at the Institute for Neuroinformatics of the University of Zurich and ETH Zurich.

Semiconductor known from solar cells

The memristors the researchers have developed are made of halide perovskite nanocrystals, a semiconductor material known primarily from its use in photovoltaic cells. “The ‘nerve conduction’ in these new memristors is mediated by temporarily or permanently stringing together silver ions from an electrode to form a nanofilament penetrating the perovskite structure through which current can flow,” explains Kovalenko.

This process can be regulated to make the silver-​ion filament either thin, so that it gradually breaks back down into individual silver ions (volatile mode), or thick and permanent (non-​volatile mode). This is controlled by the intensity of the current conducted on the memristor: applying a weak current activates the volatile mode, while a strong current activates the non-​volatile mode.

New toolkit for neuroinformaticians

“To our knowledge, this is the first memristor that can be reliably switched between volatile and non-​volatile modes on demand,” Demirağ says. This means that in the future, computer chips can be manufactured with memristors that enable both modes. This is a significance advance because it is usually not possible to combine several different types of memristors on one chip.

Within the scope of the study, which they published in the journal Nature Communications, the researchers tested 25 of these new memristors and carried out 20,000 measurements with them. In this way, they were able to simulate a computational problem on a complex network. The problem involved classifying a number of different neuron spikes as one of four predefined patterns.

Before these memristors can be used in computer technology, they will need to undergo further optimisation.  However, such components are also important for research in neuroinformatics, as Indiveri points out: “These components come closer to real neurons than previous ones. As a result, they help researchers to better test hypotheses in neuroinformatics and hopefully gain a better understanding of the computing principles of real neuronal circuits in humans and animals.”

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

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing by Rohit Abraham John, Yiğit Demirağ, Yevhen Shynkarenko, Yuliia Berezovska, Natacha Ohannessian, Melika Payvand, Peng Zeng, Maryna I. Bodnarchuk, Frank Krumeich, Gökhan Kara, Ivan Shorubalko, Manu V. Nair, Graham A. Cooke, Thomas Lippert, Giacomo Indiveri & Maksym V. Kovalenko. Nature Communications volume 13, Article number: 2074 (2022) DOI: https://doi.org/10.1038/s41467-022-29727-1 Published: 19 April 2022

This paper is open access.