Tag Archives: Armantas Melianas

2D materials for a computer’s artificial brain synapses

A January 28, 2022 news item on Nanowerk describes for some of the latest work on hardware that could enable neuromorphic (brainlike) computing. Note: A link has been removed,

Researchers from KTH Royal Institute of Technology [Sweden] and Stanford University [US] have fabricated a material for computer components that enable the commercial viability of computers that mimic the human brain (Advanced Functional Materials, “High-Speed Ionic Synaptic Memory Based on 2D Titanium Carbide MXene”).

A January 31, 2022 KTH Royal Institute of Technology press release (also on EurekAlert but published January 28, 2022), which originated the news item, delves further into the research,

Electrochemical random access (ECRAM) memory components made with 2D titanium carbide showed outstanding potential for complementing classical transistor technology, and contributing toward commercialization of powerful computers that are modeled after the brain’s neural network. Such neuromorphic computers can be thousands times more energy efficient than today’s computers.

These advances in computing are possible because of some fundamental differences from the classic computing architecture in use today, and the ECRAM, a component that acts as a sort of synaptic cell in an artificial neural network, says KTH Associate Professor Max Hamedi.

“Instead of transistors that are either on or off, and the need for information to be carried back and forth between the processor and memory—these new computers rely on components that can have multiple states, and perform in-memory computation,” Hamedi says.

The scientists at KTH and Stanford have focused on testing better materials for building an ECRAM, a component in which switching occurs by inserting ions into an oxidation channel, in a sense similar to our brain which also works with ions. What has been needed to make these chips commercially viable are materials that overcome the slow kinetics of metal oxides and the poor temperature stability of plastics.                   

The key material in the ECRAM units that the researchers fabricated is referred to as MXene—a two-dimensional (2D) compound, barely a few atoms thick, consisting of titanium carbide (Ti3C2Tx). The MXene combines the high speed of organic chemistry with the integration compatibility of inorganic materials in a single device operating at the nexus of electrochemistry and electronics, Hamedi says.

Co-author Professor Alberto Salleo at Stanford University, says that MXene ECRAMs combine the speed, linearity, write noise, switching energy, and endurance metrics essential for parallel acceleration of artificial neural networks.

“MXenes are an exciting materials family for this particular application as they combine the temperature stability needed for integration with conventional electronics with the availability of a vast composition space to optimize performance, Salleo says”

While there are many other barriers to overcome before consumers can buy their own neuromorphic computers, Hamedi says the 2D ECRAMs represent a breakthrough at least in the area of neuromorphic materials, potentially leading to artificial intelligence that can adapt to confusing input and nuance, the way the brain does with thousands time smaller energy consumption. This can also enable portable devices capable of much heavier computing tasks without having to rely on the cloud.

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

High-Speed Ionic Synaptic Memory Based on 2D Titanium Carbide MXene by
Armantas Melianas, Min-A Kang, Armin VahidMohammadi, Tyler James Quill, Weiqian Tian, Yury Gogotsi, Alberto Salleo, Mahiar Max Hamedi. Advanced Functional Materials DOI: https://doi.org/10.1002/adfm.202109970 First published: 21 November 2021

This paper is open access.

Organic neuromorphic electronics

A December 13, 2021 news item on ScienceDaily describes some research from Germany’s Max Planck Institute for Polymer Research,

The human brain works differently from a computer – while the brain works with biological cells and electrical impulses, a computer uses silicon-based transistors. Scientists have equipped a toy robot with a smart and adaptive electrical circuit made of soft organic materials, similarly to the biological matter. With this bio-inspired approach, they were able to teach the robot to navigate independently through a maze using visual signs for guidance.

A December 13, 2021 Max Planck Institute for Polymer Research press release (also on EurekAlert), which originated the news item, fills in a few details,

The processor is the brain of a computer – an often-quoted phrase. But processors work fundamentally differently than the human brain. Transistors perform logic operations by means of electronic signals. In contrast, the brain works with nerve cells, so-called neurons, which are connected via biological conductive paths, so-called synapses. At a higher level, this signaling is used by the brain to control the body and perceive the surrounding environment. The reaction of the body/brain system when certain stimuli are perceived – for example, via the eyes, ears or sense of touch – is triggered through a learning process. For example, children learn not to reach twice for a hot stove: one input stimulus leads to a learning process with a clear behavioral outcome.

Scientists working with Paschalis Gkoupidenis, group leader in Paul Blom’s department at the Max Planck Institute for Polymer Research, have now applied this basic principle of learning through experience in a simplified form and steered a robot through a maze using a so-called organic neuromorphic circuit. The work was an extensive collaboration between the Universities of Eindhoven [Eindhoven University of Technology; Netherlands], Stanford [University; California, US], Brescia [University; Italy], Oxford [UK] and KAUST [King Abdullah University of Science and Technology, Saudi Arabia].

“We wanted to use this simple setup to show how powerful such ‘organic neuromorphic devices’ can be in real-world conditions,” says Imke Krauhausen, a doctoral student in Gkoupidenis’ group and at TU Eindhoven (van de Burgt group), and first author of the scientific paper.

To achieve the navigation of the robot inside the maze, the researchers fed the smart adaptive circuit with sensory signals coming from the environment. The path of maze towards the exit is indicated visually at each maze intersects. Initially, the robot often misinterprets the visual signs, thus it makes the wrong “turning” decisions at the maze intersects and loses the way out. When the robot takes these decisions and follows wrong dead-end paths, it is being discouraged to take these wrong decisions by receiving corrective stimuli. The corrective stimuli, for example when the robot hits a wall, are directly applied at the organic circuit via electrical signals induced by a touch sensor attached to the robot. With each subsequent execution of the experiment, the robot gradually learns to make the right “turning” decisions at the intersects, i. e. to avoid receiving corrective stimuli, and after a few trials it finds the way out of the maze. This learning process happens exclusively on the organic adaptive circuit. 

“We were really glad to see that the robot can pass through the maze after some runs by learning on a simple organic circuit. We have shown here a first, very simple setup. In the distant future, however, we hope that organic neuromorphic devices could also be used for local and distributed computing/learning. This will open up entirely new possibilities for applications in real-world robotics, human-machine interfaces and point-of-care diagnostics. Novel platforms for rapid prototyping and education, at the intersection of materials science and robotics, are also expected to emerge.” Gkoupidenis says.

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

Organic neuromorphic electronics for sensorimotor integration and learning in robotics by Imke Krauhausen, Dimitrios A. Koutsouras, Armantas Melianas, Scott T. Keene, Katharina Lieberth, Hadrien Ledanseur, Rajendar Sheelamanthula, Alexander Giovannitti, Fabrizio Torricelli, Iain Mcculloch, Paul W. M. Blom, Alberto Salleo, Yoeri van de Burgt and Paschalis Gkoupidenis. Science Advances • 10 Dec 2021 • Vol 7, Issue 50 • DOI: 10.1126/sciadv.abl5068

This paper is open access.

A biohybrid artificial synapse that can communicate with living cells

As I noted in my June 16, 2020 posting, we may have more than one kind of artificial brain in our future. This latest work features a biohybrid. From a June 15, 2020 news item on ScienceDaily,

In 2017, Stanford University researchers presented a new device that mimics the brain’s efficient and low-energy neural learning process [see my March 8, 2017 posting for more]. It was an artificial version of a synapse — the gap across which neurotransmitters travel to communicate between neurons — made from organic materials. In 2019, the researchers assembled nine of their artificial synapses together in an array, showing that they could be simultaneously programmed to mimic the parallel operation of the brain [see my Sept. 17, 2019 posting].

Now, in a paper published June 15 [2020] in Nature Materials, they have tested the first biohybrid version of their artificial synapse and demonstrated that it can communicate with living cells. Future technologies stemming from this device could function by responding directly to chemical signals from the brain. The research was conducted in collaboration with researchers at Istituto Italiano di Tecnologia (Italian Institute of Technology — IIT) in Italy and at Eindhoven University of Technology (Netherlands).

“This paper really highlights the unique strength of the materials that we use in being able to interact with living matter,” said Alberto Salleo, professor of materials science and engineering at Stanford and co-senior author of the paper. “The cells are happy sitting on the soft polymer. But the compatibility goes deeper: These materials work with the same molecules neurons use naturally.”

While other brain-integrated devices require an electrical signal to detect and process the brain’s messages, the communications between this device and living cells occur through electrochemistry — as though the material were just another neuron receiving messages from its neighbor.

A June 15, 2020 Stanford University news release (also on EurekAlert) by Taylor Kubota, which originated the news item, delves further into this recent work,

How neurons learn

The biohybrid artificial synapse consists of two soft polymer electrodes, separated by a trench filled with electrolyte solution – which plays the part of the synaptic cleft that separates communicating neurons in the brain. When living cells are placed on top of one electrode, neurotransmitters that those cells release can react with that electrode to produce ions. Those ions travel across the trench to the second electrode and modulate the conductive state of this electrode. Some of that change is preserved, simulating the learning process occurring in nature.

“In a biological synapse, essentially everything is controlled by chemical interactions at the synaptic junction. Whenever the cells communicate with one another, they’re using chemistry,” said Scott Keene, a graduate student at Stanford and co-lead author of the paper. “Being able to interact with the brain’s natural chemistry gives the device added utility.”

This process mimics the same kind of learning seen in biological synapses, which is highly efficient in terms of energy because computing and memory storage happen in one action. In more traditional computer systems, the data is processed first and then later moved to storage.

To test their device, the researchers used rat neuroendocrine cells that release the neurotransmitter dopamine. Before they ran their experiment, they were unsure how the dopamine would interact with their material – but they saw a permanent change in the state of their device upon the first reaction.

“We knew the reaction is irreversible, so it makes sense that it would cause a permanent change in the device’s conductive state,” said Keene. “But, it was hard to know whether we’d achieve the outcome we predicted on paper until we saw it happen in the lab. That was when we realized the potential this has for emulating the long-term learning process of a synapse.”

A first step

This biohybrid design is in such early stages that the main focus of the current research was simply to make it work.

“It’s a demonstration that this communication melding chemistry and electricity is possible,” said Salleo. “You could say it’s a first step toward a brain-machine interface, but it’s a tiny, tiny very first step.”

Now that the researchers have successfully tested their design, they are figuring out the best paths for future research, which could include work on brain-inspired computers, brain-machine interfaces, medical devices or new research tools for neuroscience. Already, they are working on how to make the device function better in more complex biological settings that contain different kinds of cells and neurotransmitters.

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

A biohybrid synapse with neurotransmitter-mediated plasticity by Scott T. Keene, Claudia Lubrano, Setareh Kazemzadeh, Armantas Melianas, Yaakov Tuchman, Giuseppina Polino, Paola Scognamiglio, Lucio Cinà, Alberto Salleo, Yoeri van de Burgt & Francesca Santoro. Nature Materials (2020) DOI: https://doi.org/10.1038/s41563-020-0703-y Published: 15 June 2020

This paper is behind a paywall.

Bad battery, good synapse from Stanford University

A May 4, 2019 news item on ScienceDaily announces the latest advance made by Stanford University and Sandia National Laboratories in the field of neuromorphic (brainlike) computing,

The brain’s capacity for simultaneously learning and memorizing large amounts of information while requiring little energy has inspired an entire field to pursue brain-like — or neuromorphic — computers. Researchers at Stanford University and Sandia National Laboratories previously developed one portion of such a computer: a device that acts as an artificial synapse, mimicking the way neurons communicate in the brain.

In a paper published online by the journal Science on April 25 [2019], the team reports that a prototype array of nine of these devices performed even better than expected in processing speed, energy efficiency, reproducibility and durability.

Looking forward, the team members want to combine their artificial synapse with traditional electronics, which they hope could be a step toward supporting artificially intelligent learning on small devices.

“If you have a memory system that can learn with the energy efficiency and speed that we’ve presented, then you can put that in a smartphone or laptop,” said Scott Keene, co-author of the paper and a graduate student in the lab of Alberto Salleo, professor of materials science and engineering at Stanford who is co-senior author. “That would open up access to the ability to train our own networks and solve problems locally on our own devices without relying on data transfer to do so.”

An April 25, 2019 Stanford University news release (also on EurekAlert but published May 3, 2019) by Taylor Kubota, which originated the news item, expands on the theme,

A bad battery, a good synapse

The team’s artificial synapse is similar to a battery, modified so that the researchers can dial up or down the flow of electricity between the two terminals. That flow of electricity emulates how learning is wired in the brain. This is an especially efficient design because data processing and memory storage happen in one action, rather than a more traditional computer system where the data is processed first and then later moved to storage.

Seeing how these devices perform in an array is a crucial step because it allows the researchers to program several artificial synapses simultaneously. This is far less time consuming than having to program each synapse one-by-one and is comparable to how the brain actually works.

In previous tests of an earlier version of this device, the researchers found their processing and memory action requires about one-tenth as much energy as a state-of-the-art computing system needs in order to carry out specific tasks. Still, the researchers worried that the sum of all these devices working together in larger arrays could risk drawing too much power. So, they retooled each device to conduct less electrical current – making them much worse batteries but making the array even more energy efficient.

The 3-by-3 array relied on a second type of device – developed by Joshua Yang at the University of Massachusetts, Amherst, who is co-author of the paper – that acts as a switch for programming synapses within the array.

“Wiring everything up took a lot of troubleshooting and a lot of wires. We had to ensure all of the array components were working in concert,” said Armantas Melianas, a postdoctoral scholar in the Salleo lab. “But when we saw everything light up, it was like a Christmas tree. That was the most exciting moment.”

During testing, the array outperformed the researchers’ expectations. It performed with such speed that the team predicts the next version of these devices will need to be tested with special high-speed electronics. After measuring high energy efficiency in the 3-by-3 array, the researchers ran computer simulations of a larger 1024-by-1024 synapse array and estimated that it could be powered by the same batteries currently used in smartphones or small drones. The researchers were also able to switch the devices over a billion times – another testament to its speed – without seeing any degradation in its behavior.

“It turns out that polymer devices, if you treat them well, can be as resilient as traditional counterparts made of silicon. That was maybe the most surprising aspect from my point of view,” Salleo said. “For me, it changes how I think about these polymer devices in terms of reliability and how we might be able to use them.”

Room for creativity

The researchers haven’t yet submitted their array to tests that determine how well it learns but that is something they plan to study. The team also wants to see how their device weathers different conditions – such as high temperatures – and to work on integrating it with electronics. There are also many fundamental questions left to answer that could help the researchers understand exactly why their device performs so well.

“We hope that more people will start working on this type of device because there are not many groups focusing on this particular architecture, but we think it’s very promising,” Melianas said. “There’s still a lot of room for improvement and creativity. We only barely touched the surface.”

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

Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing by Elliot J. Fuller, Scott T. Keene, Armantas Melianas, Zhongrui Wang, Sapan Agarwal, Yiyang Li, Yaakov Tuchman, Conrad D. James, Matthew J. Marinella, J. Joshua Yang3, Alberto Salleo, A. Alec Talin1. Science 25 Apr 2019: eaaw5581 DOI: 10.1126/science.aaw5581

This paper is behind a paywall.

For anyone interested in more about brainlike/brain-like/neuromorphic computing/neuromorphic engineering/memristors, use any or all of those terms in this blog’s search engine.