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Synaptic transistor better then memristor when it comes to brainlike learning for computers

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An April 30, 2021 news item on Nanowerk announced research from a joint team at Northwestern University (located in Chicago, Illinois, US) and University of Hong Kong of researchers in the field of neuromorphic (brainlike) computing,

Researchers have developed a brain-like computing device that is capable of learning by association.

Similar to how famed physiologist Ivan Pavlov conditioned dogs to associate a bell with food, researchers at Northwestern University and the University of Hong Kong successfully conditioned their circuit to associate light with pressure.

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The device’s secret lies within its novel organic, electrochemical “synaptic transistors,” which simultaneously process and store information just like the human brain. The researchers demonstrated that the transistor can mimic the short-term and long-term plasticity of synapses in the human brain, building on memories to learn over time.

With its brain-like ability, the novel transistor and circuit could potentially overcome the limitations of traditional computing, including their energy-sapping hardware and limited ability to perform multiple tasks at the same time. The brain-like device also has higher fault tolerance, continuing to operate smoothly even when some components fail.

“Although the modern computer is outstanding, the human brain can easily outperform it in some complex and unstructured tasks, such as pattern recognition, motor control and multisensory integration,” said Northwestern’s Jonathan Rivnay, a senior author of the study. “This is thanks to the plasticity of the synapse, which is the basic building block of the brain’s computational power. These synapses enable the brain to work in a highly parallel, fault tolerant and energy-efficient manner. In our work, we demonstrate an organic, plastic transistor that mimics key functions of a biological synapse.”

Rivnay is an assistant professor of biomedical engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Paddy Chan, an associate professor of mechanical engineering at the University of Hong Kong. Xudong Ji, a postdoctoral researcher in Rivnay’s group, is the paper’s first author.

Caption: By connecting single synaptic transistors into a neuromorphic circuit, researchers demonstrated that their device could simulate associative learning. Credit: Northwestern University

An April 30, 2021 Northwestern University news release (also on EurekAlert), which originated the news item, includes a good explanation about brainlike computing and information about how synaptic transistors work along with some suggestions for future applications,

Conventional, digital computing systems have separate processing and storage units, causing data-intensive tasks to consume large amounts of energy. Inspired by the combined computing and storage process in the human brain, researchers, in recent years, have sought to develop computers that operate more like the human brain, with arrays of devices that function like a network of neurons.

“The way our current computer systems work is that memory and logic are physically separated,” Ji said. “You perform computation and send that information to a memory unit. Then every time you want to retrieve that information, you have to recall it. If we can bring those two separate functions together, we can save space and save on energy costs.”

Currently, the memory resistor, or “memristor,” is the most well-developed technology that can perform combined processing and memory function, but memristors suffer from energy-costly switching and less biocompatibility. These drawbacks led researchers to the synaptic transistor — especially the organic electrochemical synaptic transistor, which operates with low voltages, continuously tunable memory and high compatibility for biological applications. Still, challenges exist.

“Even high-performing organic electrochemical synaptic transistors require the write operation to be decoupled from the read operation,” Rivnay said. “So if you want to retain memory, you have to disconnect it from the write process, which can further complicate integration into circuits or systems.”

How the synaptic transistor works

To overcome these challenges, the Northwestern and University of Hong Kong team optimized a conductive, plastic material within the organic, electrochemical transistor that can trap ions. In the brain, a synapse is a structure through which a neuron can transmit signals to another neuron, using small molecules called neurotransmitters. In the synaptic transistor, ions behave similarly to neurotransmitters, sending signals between terminals to form an artificial synapse. By retaining stored data from trapped ions, the transistor remembers previous activities, developing long-term plasticity.

The researchers demonstrated their device’s synaptic behavior by connecting single synaptic transistors into a neuromorphic circuit to simulate associative learning. They integrated pressure and light sensors into the circuit and trained the circuit to associate the two unrelated physical inputs (pressure and light) with one another.

Perhaps the most famous example of associative learning is Pavlov’s dog, which naturally drooled when it encountered food. After conditioning the dog to associate a bell ring with food, the dog also began drooling when it heard the sound of a bell. For the neuromorphic circuit, the researchers activated a voltage by applying pressure with a finger press. To condition the circuit to associate light with pressure, the researchers first applied pulsed light from an LED lightbulb and then immediately applied pressure. In this scenario, the pressure is the food and the light is the bell. The device’s corresponding sensors detected both inputs.

After one training cycle, the circuit made an initial connection between light and pressure. After five training cycles, the circuit significantly associated light with pressure. Light, alone, was able to trigger a signal, or “unconditioned response.”

Future applications

Because the synaptic circuit is made of soft polymers, like a plastic, it can be readily fabricated on flexible sheets and easily integrated into soft, wearable electronics, smart robotics and implantable devices that directly interface with living tissue and even the brain [emphasis mine].

“While our application is a proof of concept, our proposed circuit can be further extended to include more sensory inputs and integrated with other electronics to enable on-site, low-power computation,” Rivnay said. “Because it is compatible with biological environments, the device can directly interface with living tissue, which is critical for next-generation bioelectronics.”

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

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor by Xudong Ji, Bryan D. Paulsen, Gary K. K. Chik, Ruiheng Wu, Yuyang Yin, Paddy K. L. Chan & Jonathan Rivnay . Nature Communications volume 12, Article number: 2480 (2021) DOI: https://doi.org/10.1038/s41467-021-22680-5 Published: 30 April 2021

This paper is open access.

“… devices that directly interface with living tissue and even the brain,” would I be the only one thinking about cyborgs?

Predicting how a memristor functions

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An April 3, 2017 news item on Nanowerk announces a new memristor development (Note: A link has been removed),

Researchers from the CNRS [Centre national de la recherche scientifique; France] , Thales, and the Universities of Bordeaux, Paris-Sud, and Evry have created an artificial synapse capable of learning autonomously. They were also able to model the device, which is essential for developing more complex circuits. The research was published in Nature Communications (“Learning through ferroelectric domain dynamics in solid-state synapses”)

An April 3, 2017 CNRS press release, which originated the news item, provides a nice introduction to the memristor concept before providing a few more details about this latest work (Note: A link has been removed),

One of the goals of biomimetics is to take inspiration from the functioning of the brain [also known as neuromorphic engineering or neuromorphic computing] in order to design increasingly intelligent machines. This principle is already at work in information technology, in the form of the algorithms used for completing certain tasks, such as image recognition; this, for instance, is what Facebook uses to identify photos. However, the procedure consumes a lot of energy. Vincent Garcia (Unité mixte de physique CNRS/Thales) and his colleagues have just taken a step forward in this area by creating directly on a chip an artificial synapse that is capable of learning. They have also developed a physical model that explains this learning capacity. This discovery opens the way to creating a network of synapses and hence intelligent systems requiring less time and energy.

Our brain’s learning process is linked to our synapses, which serve as connections between our neurons. The more the synapse is stimulated, the more the connection is reinforced and learning improved. Researchers took inspiration from this mechanism to design an artificial synapse, called a memristor. This electronic nanocomponent consists of a thin ferroelectric layer sandwiched between two electrodes, and whose resistance can be tuned using voltage pulses similar to those in neurons. If the resistance is low the synaptic connection will be strong, and if the resistance is high the connection will be weak. This capacity to adapt its resistance enables the synapse to learn.

Although research focusing on these artificial synapses is central to the concerns of many laboratories, the functioning of these devices remained largely unknown. The researchers have succeeded, for the first time, in developing a physical model able to predict how they function. This understanding of the process will make it possible to create more complex systems, such as a series of artificial neurons interconnected by these memristors.

As part of the ULPEC H2020 European project, this discovery will be used for real-time shape recognition using an innovative camera1 : the pixels remain inactive, except when they see a change in the angle of vision. The data processing procedure will require less energy, and will take less time to detect the selected objects. The research involved teams from the CNRS/Thales physics joint research unit, the Laboratoire de l’intégration du matériau au système (CNRS/Université de Bordeaux/Bordeaux INP), the University of Arkansas (US), the Centre de nanosciences et nanotechnologies (CNRS/Université Paris-Sud), the Université d’Evry, and Thales.

 

Image synapse

© Sören Boyn / CNRS/Thales physics joint research unit.

Artist’s impression of the electronic synapse: the particles represent electrons circulating through oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses.

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

Learning through ferroelectric domain dynamics in solid-state synapses by Sören Boyn, Julie Grollier, Gwendal Lecerf, Bin Xu, Nicolas Locatelli, Stéphane Fusil, Stéphanie Girod, Cécile Carrétéro, Karin Garcia, Stéphane Xavier, Jean Tomas, Laurent Bellaiche, Manuel Bibes, Agnès Barthélémy, Sylvain Saïghi, & Vincent Garcia. Nature Communications 8, Article number: 14736 (2017) doi:10.1038/ncomms14736 Published online: 03 April 2017

This paper is open access.

Thales or Thales Group is a French company, from its Wikipedia entry (Note: Links have been removed),

Thales Group (French: [talɛs]) is a French multinational company that designs and builds electrical systems and provides services for the aerospace, defence, transportation and security markets. Its headquarters are in La Défense[2] (the business district of Paris), and its stock is listed on the Euronext Paris.

The company changed its name to Thales (from the Greek philosopher Thales,[3] pronounced [talɛs] reflecting its pronunciation in French) from Thomson-CSF in December 2000 shortly after the £1.3 billion acquisition of Racal Electronics plc, a UK defence electronics group. It is partially state-owned by the French government,[4] and has operations in more than 56 countries. It has 64,000 employees and generated €14.9 billion in revenues in 2016. The Group is ranked as the 475th largest company in the world by Fortune 500 Global.[5] It is also the 10th largest defence contractor in the world[6] and 55% of its total sales are military sales.[4]

The ULPEC (Ultra-Low Power Event-Based Camera) H2020 [Horizon 2020 funded) European project can be found here,

The long term goal of ULPEC is to develop advanced vision applications with ultra-low power requirements and ultra-low latency. The output of the ULPEC project is a demonstrator connecting a neuromorphic event-based camera to a high speed ultra-low power consumption asynchronous visual data processing system (Spiking Neural Network with memristive synapses). Although ULPEC device aims to reach TRL 4, it is a highly application-oriented project: prospective use cases will b…

Finally, for anyone curious about Thales, the philosopher (from his Wikipedia entry), Note: Links have been removed,

Thales of Miletus (/ˈθeɪliːz/; Greek: Θαλῆς (ὁ Μῑλήσιος), Thalēs; c. 624 – c. 546 BC) was a pre-Socratic Greek/Phoenician philosopher, mathematician and astronomer from Miletus in Asia Minor (present-day Milet in Turkey). He was one of the Seven Sages of Greece. Many, most notably Aristotle, regard him as the first philosopher in the Greek tradition,[1][2] and he is otherwise historically recognized as the first individual in Western civilization known to have entertained and engaged in scientific philosophy.[3][4]

Electrochemical measurements of biomolecules

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This work comes from Finland and features some new nano shapes. From a Nov. 10, 2016 news item on phys.org,

Tomi Laurila’s research topic has many quirky names.

“Nanodiamond, nanohorn, nano-onion…,” lists off the Aalto University Professor, recounting the many nano-shapes of carbon. Laurila is using these shapes to build new materials: tiny sensors, only a few hundred nanometres across, that can achieve great things due to their special characteristics.

For one, the sensors can be used to enhance the treatment of neurological conditions. That is why Laurila, University of Helsinki Professor Tomi Taira and experts from HUS (the Hospital District of Helsinki and Uusimaa) are looking for ways to use the sensors for taking electrochemical measurements of biomolecules. Biomolecules are e.g. neurotransmitters such as glutamate, dopamine and opioids, which are used by nerve cells to communicate with each other.

A Nov. 10, 2016 Aalto University press release, which originated the news item, expands on the theme,

Most of the drugs meant for treating neurological diseases change the communication between nerve cells that is based on neurotransmitters. If we had real time and individual information on the operation of the neurotransmitter system, it would make it much easier to for example plan precise treatments’, explains Taira.

Due to their small size, carbon sensors can be taken directly next to a nerve cell, where the sensors will report what kind of neurotransmitter the cell is emitting and what kind of reaction it is inducing in other cells.

‘In practice, we are measuring the electrons that are moving in oxidation and reduction reactions’, Laurila explains the operating principle of the sensors.

‘The advantage of the sensors developed by Tomi and the others is their speed and small size. The probes used in current measurement methods can be compared to logs on a cellular scale – it’s impossible to use them and get an idea of the brain’s dynamic’, summarizes Taira.

Feedback system and memory traces

For the sensors, the journey from in vitro tests conducted in glass dishes and test tubes to in vivo tests and clinical use is long. However, the researchers are highly motivated.

‘About 165 million people are suffering from various neurological diseases in Europe alone. And because they are so expensive to treat, neurological diseases make up as much as 80 per cent of health care costs’, tells Taira.

Tomi Laurila believes that carbon sensors will have applications in fields such as optogenetics. Optogenetics is a recently developed method where a light-sensitive molecule is brought into a nerve cell so that the cell’s electric operation can then be turned on or off by stimulating it with light. A few years ago, a group of scientists proved in the scientific journal Nature that they had managed to use optogenetics to activate a memory trace that had been created previously due to learning. Using the same technique, researchers were able to demonstrate that with a certain type of Alzheimer’s, the problem is not that there are no memory traces being created, but that the brain cannot read the traces.

‘So the traces exist, and they can be activated by boosting them with light stimuli’, explains Taira but stresses that a clinical application is not yet a reality. However, clinical applications for other conditions may be closer by. One example is Parkinson’s disease. In Parkinson’s disease, the amount of dopamine starts to decrease in the cells of a particular brain section, which causes the typical symptoms such as tremors, rigidity and slowness of movement. With the sensors, the level of dopamine could be monitored in real time.

‘A sort of feedback system could be connected to it, so that it would react by giving an electric or optical stimulus to the cells, which would in turn release more dopamine’, envisions Taira.

‘Another application that would have an immediate clinical use is monitoring unconscious and comatose patients. With these patients, the level of glutamate fluctuates very much, and too much glutamate damages the nerve cell – online monitoring would therefore improve their treatment significantly.

Atom by atom

Manufacturing carbon sensors is definitely not a mass production process; it is slow and meticulous handiwork.

‘At this stage, the sensors are practically being built atom by atom’, summarises Tomi Laurila.

‘Luckily, we have many experts on carbon materials of our own. For example, the nanobuds of Professor Esko Kauppinen and the carbon films of Professor Jari Koskinen help with the manufacturing of the sensors. Carbon-based materials are mainly very compatible with the human body, but there is still little information about them. That’s why a big part of the work is to go through the electrochemical characterisation that has been done on different forms of carbon.’

The sensors are being developed and tested by experts from various fields, such as chemistry, materials science, modelling, medicine and imaging. Twenty or so articles have been published on the basic properties of the materials. Now, the challenge is to build them into geometries that are functional in a physiological environment. And taking measurements is not simple, either.

‘Brain tissue is delicate and doesn’t appreciate having objects being inserted in it. But if this were easy, someone would’ve already done it’, conclude the two.

I wish the researchers good luck.