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Chips based on nanofluidic synapses use ions instead of electrons to process data

In another step toward nanofluidic-based neuromorphic (brain-inspired) computing, EPFL (École polytechnique fédérale de Lausanne) engineers have executed a logic operation by connecting chips that use ions rather than electrons to process data, avoiding the rising energy cost of computers.

On Friday, March 14, Mindplex News presented two approaches to creating synapse-like more efficient analog memristors (memory resistors): a paper-based photoelectronic device and a circuit crossbar array.

Nanofluidic memristive device

As published in Nature Electronics today (March 19, 2024), researchers at the EPFL Laboratory of Nanoscale Biology have explored a third approach: a nanofluidic memristive device that relies on ions in a nanofluidic neural network, closely mimicking the brain.

“We have fabricated a new nanofluidic device for memory applications that is significantly more scalable and much more performant than previous attempts,” says LBEN postdoctoral researcher Théo Emmerich. “This has enabled us, for the very first time, to connect two such ‘artificial synapses,’ paving the way for the design of brain-inspired liquid hardware.”

Just add water

For their study, the researchers immersed their device in an electrolyte water solution containing potassium ions, but others could be used, including sodium and calcium.

“We can tune the memory of our device by changing the ions we use, which affects how it switches from on to off, or how much memory it stores,” Emmerich explains.

The device was fabricated on a chip at EPFL’s Center of MicroNanoTechnology by creating a nanopore* at the center of a silicon nitride membrane, inside a synapse.*

Liquid Circuits

Their next goal is to connect a network of highly asymmetric channels (HACs) with water channels to create fully liquid circuits. In addition to providing an in-built cooling mechanism, the use of water would facilitate the development of biocompatible devices, with potential applications in brain-computer interfaces or neuromedicine.

* The researchers added palladium and graphite layers to create nano-channels for ions. As a current flows through the chip, the ions percolate through the channels and converge at the pore, where their pressure creates a blister between the chip surface and the graphite. As the graphite layer is forced up by the blister, the device becomes more conductive, switching its memory state to ‘on’. Since the graphite layer stays lifted, even without a current, the device ‘remembers’ its previous state.  A negative voltage puts the layers back into contact, resetting the memory to the ‘off’ state.

Citation: Emmerich, T., Teng, Y., Ronceray, N. et al. Nanofluidic logic with mechano–ionic memristive switches. Nat Electron (2024). https://doi.org/10.1038/s41928-024-01137-9 (open-access)

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StyleTalker takes one photo of you and produces a video of you talking

Scientists from KAIST, the South Korean AI Institute, have described a new model called StyleTalker, which takes a single image of a person as an input, and produces a video of them talking “with accurately audio-synced lip shapes, realistic head poses, and eye blinks”.

StyleTalker combines AI techniques for “audio-driven generation” (generating realistic lip-movements from audio) with “motion-controllable” generation, that can do things like take the head-movements and gestures from one video, and use them in a new video with a new face.

The work builds on a recent boom in neural lip-synced video generation, a research-field that aims to “transforming the lip region of the person in the target video,generating new videos with the lip shapes that match the input audio.” (This could be used, for example, when movies are dubbed from one language into another.)

StyleTalker “can generate more natural and robust talking head videos compared to other models” previously described. It is a step towards more realistic fake videos, but is that a good thing? Let us know in the comments how you think this technology could be used for good and for bad.

Citation: Dongchan Min, Minyoung Song, Eunji Ko, Sung Ju Hwang. StyleTalker: One-shot Style-based Audio-driven Talking Head Video Generation. eprint (2024). https://arxiv.org/abs/2208.10922 (open access)

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How to speak without active vocal cords

UCLA engineers have invented a soft, thin, stretchy device measuring just over 1 square inch that can be attached to the skin outside the throat to help people with dysfunctional vocal cords regain their voice function.

The development is described in the open-access journal Nature Communications.

The new bioelectric system, developed by Jun Chen, an assistant professor of bioengineering at the UCLA Samueli School of Engineering, and his colleagues, can detect movement in a person’s larynx muscles and translate those signals into audible speech, with the assistance of machine-learning technology and with nearly 95% accuracy.

How it works

The tiny new patch-like device is made up of two components.

Converting laryngeal muscle movement into analyzable electrical signals (credit: Jun Chen Lab/UCLA)

A self-powered sensing component detects and converts signals generated by muscle movements into high-fidelity, analyzable electrical signals. These are then translated into speech signals, using a machine-learning algorithm. The other component turns those speech signals into the desired voice expression.  

Stretchable, waterproof magnetoelastic generator (credit: Jun Chen Lab/UCLA)

The device uses a soft magnetoelastic sensing mechanism, developed by Chen’s team in 2021, to detect changes in the magnetic field when it is altered as a result of mechanical forces—in this case, the movement of laryngeal muscles. The embedded serpentine induction coils in the magnetoelastic layers help generate high-fidelity electrical signals for sensing purposes.

With double-sided biocompatible tape, the device can easily adhere to an individual’s throat near the location of the vocal cords and can be reused by reapplying tape as needed.

Machine learning

In their experiments, the researchers tested the wearable technology on eight healthy adults. They collected data on laryngeal muscle movement and used a machine-learning algorithm to correlate the resulting signals to certain words. They then selected a corresponding output voice signal through the device’s actuation component.

The research team demonstrated the system’s accuracy by having the participants pronounce five sentences—both aloud and voicelessly—including “Hi, Rachel, how are you doing today?” and “I love you!”

The overall prediction accuracy of the model was 94.68%, with the participants’ voice signal amplified by the actuation component, and demonstrating that the sensing mechanism recognized their laryngeal movement signal and matched the corresponding sentence the participants wished to say.

The research team plans to continue enlarging the vocabulary of the device through machine learning and to test it in people with speech disorders.

The team previously developed a wearable glove capable of translating American Sign Language into English speech in real time to help users of ASL communicate with those who don’t know how to sign.

The research was funded by the National Institutes of Health, the U.S. Office of Naval Research, the American Heart Association, Brain & Behavior Research Foundation, the UCLA Clinical and Translational Science Institute, and the UCLA Samueli School of Engineering.

Citation: Che, Z., Wan, X., Xu, J. et al. Speaking withoutCitation: Che, Z., Wan, X., Xu, J. et al. Speaking without vocal folds using a machine-learning-assisted wearable sensing-actuation system. Nat Commun 15, 1873 (2024). Vocal folds using a machine-learning-assisted wearable sensing-actuation system. Nat Commun 15, 1873 (2024). https://www.nature.com/articles/s41467-024-45915-7 (open-access)

Thumbnail image credit: Jun Chen Lab/UCLA

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Analog computing solves complex equations using far less energy 

A team of researchers demonstrates that a memristor device can solve complex scientific problems by using significantly less energy—overcoming one of the major hurdles of digital computing. [Happy Pi Day!]

Overcoming a traffic jam

Many of today’s important scientific questions can be explored using complex equations. But today’s digital computing systems are reaching their limit for performing these computations in terms of speed, energy consumption and infrastructure.

Accordng to Qiangfei Xia, UMass Amherst professor of electrical and computer engineering, one of the corresponding authors of a paper in the journal Science, with current computing methods, every time you want to store information or give a computer a task, it requires moving between memory and computing units. With complex tasks moving larger amounts of data, you essentially get a processing “traffic jam,” Xia says.

Reducing data transfers

Traditional computing has aimed to solve this is by increasing expensive bandwidth. Instead, Xia and his colleagues at UMass Amherst, the University of Southern California, and computing technology maker, TetraMem Inc. have implemented in-memory computing, using analog memristor technology to avoid these bottlenecks by reducing the number of data transfers.

The team’s in-memory computing relies on an electrical component called a memristor (a combination of “memory” and “resistor”). It controls the flow of electrical current in a circuit, while also “remembering” the prior state (even when the power is turned off). Today’s transistor-based computer chips can only hold information while there is power.

When organized into a “crossbar array”, a memristive circuit does analog computing, using physical laws in a massively parallel fashion, substantially accelerating matrix operation, the most frequently used but very power-hungry computation in neural networks.*

Reducing energy while speeding up computation

In their new research, “we propose and demonstrate a new circuit architecture and programming protocol that can efficiently represent high-precision numbers using relatively low-precision analog devices, such as memristors, with a greatly reduced overhead in circuitry, energy and latency, compared with existing quantization approaches,” says Xia.

“The breakthrough for this particular paper is that we push the boundary further,” he adds. “This technology is not only good for low-precision, neural network computing, but it can also be good for high-precision, scientific computing.”

“Our research in the past decade has made analog memristor a viable technology. It is time to move such a great technology into the semiconductor industry to benefit the broad AI hardware community”, Xia suggests.

Citation: Song, W., Rao, M., Li, Y., Li, C., Zhuo, Y., Cai, F., Wu, M., Yin, W., Li, Z., Wei, Q., Lee, S., Zhu, H., Gong, L., Barnell, M., Wu, Q., Beerel, P. A., Shuo-Wei Chen, M., Ge, N., Hu, M., . . . Yang, J. J. (2024). Programming memristor arrays with arbitrarily high precision for analog computing. Science. https://www.science.org/doi/10.1126/science.adi9405

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Paper-based synapse-like sensor for health monitoring

Researchers in Japan have developed a flexible paper-based sensor that operates like the human brain.

The optoelectronic synaptic device, using physical reservoir computing, exhibits synaptic behavior and supports cognitive tasks at a timescale suitable for health monitoring,” says Dr. Takahashi Ikuno, senior author of a paper published online in the journal Advanced Electronic Materials.

An AI-based health monitoring and biological diagnosis device

To achieve low-power consumption for prolonged use, the AI-based health monitoring and biological diagnosis device uses a standalone sensor that operates independently, with no need for constant connection to a central server, explains Ikuno.

“It should also be capable of handling rapidly changing biological signals for real-time monitoring, be flexible enough to attach comfortably to the human body, and be easy to make and dispose frequent replacements for hygiene reasons.”

Artificial synapse design

To achieve those goals, the researchers fabricated a photoelectronic artificial synapse device, comprising gold electrodes on top of a 10 µm transparent film consisting of zinc oxide (ZnO) nanoparticles and cellulose nanofibers (CNFs).

The transparent film serves three main purposes:

It allows light to pass through, enabling it to handle optical input signals representing various biological information.

The cellulose nanofibers impart flexibility and can be easily disposed by incineration.

Synapse-like design

The zinc oxide nanoparticles are photoresponsive and generate a photocurrent when exposed to pulsed UV light at a constant voltage. This photocurrent mimics the responses transmitted by synapses in the human brain, enabling the device to interpret and process biological information received from optical sensors.

Notably, the film was able to distinguish 4-bit input optical pulses and generate distinct currents in response to time-series optical input, with a rapid response time on the order of subseconds. This quick response is crucial for detecting sudden changes or abnormalities in health-related signals.

When exposed to two successive light pulses, the electrical current response was stronger for the second pulse. This behavior termed post-potentiation facilitation contributes to short-term memory processes in the brain and enhances the ability of synapses to detect and respond to familiar patterns.

To test this, the researchers converted MNIST images (a standard dataset of handwritten digits) into 4-bit optical pulses. They then irradiated the film with these pulses and measured the current response. Using this data as input, the neural network was able to recognize handwritten numbers with an accuracy of 88%.

Wearable sensors for health monitoring

Remarkably, this handwritten-digit recognition capability remained unaffected, even when the device was repeatedly bent and stretched up to 1,000 times, demonstrating its ruggedness and feasibility for repeated use. 

“This study highlights the potential of embedding semiconductor nanoparticles in flexible CNF films for use as flexible synaptic devices for PRC,” concludes Ikuno. The research paves the way for wearable sensors in health monitoring applications, he suggests.

Citation: Komatsu, H., Hosoda, N., Kounoue, T., Tokiwa, K., & Ikuno, T. Disposable and Flexible Paper-Based Optoelectronic Synaptic Devices for Physical Reservoir Computing. Advanced Electronic Materials, 2300749. https://doi.org/10.1002/aelm.202300749 (open-access)

                  

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Therapeutic potential of natural mushroom compounds to address psychiatric disorders

Natural psilocybin-containing mushroom extract might demonstrate superior efficacy compared to chemically synthesized psilocybin, suggest researchers from the Hebrew University-Hadassah Medical Center.

Therapeutic use

In their study, published in the Springer Nature journal Molecular Psychiatry, the plant extract increased the levels of synaptic proteins associated with neuroplasticity in key brain regions, including the frontal cortex, hippocampus, amygdala, and striatum.

The researchers found that psilocybin-containing mushroom extract may offer unique therapeutic effects not achievable with psilocybin alone, tsuch as treating depression, PTSD, and schizophrenia.

Controlled cultivation

Ancient medicinal practices use extracts or entire products, such as consuming the entire mushroom. These practices could open up new possibilities for the therapeutic use of natural psychedelic compounds, the researchers suggest.

However, a major challenge with natural extracts is achieving a consistently stable compound profile with plants, the researchers note. Mushroom compounds are highly influenced by their growing environment, substrate composition, CO2/O2 ratio, light exposure, temperature, and microbial surroundings.

But controlled cultivation may allow for taming the mushrooms, enabling them to produce a replicable extract, the researchers suggest.

Citation: Shahar, O., Botvinnik, A., Shwartz, A. et al. Effect of chemically synthesized psilocybin and psychedelic mushroom extract on molecular and metabolic profiles in mouse brain. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02477-w (open access)

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Robotic interface masters a soft touch

EPFL researchers have developed a haptic (relating to the sense of touch) technology that is capable of reproducing the softness of materials ranging from a marshmallow to a beating heart —overcoming a deceptively complex challenge that has previously eluded roboticists.

The perception of softness plays a crucial role in many actions and interactions—from judging the ripeness of an avocado to conducting a medical exam, or holding the hand of a loved one. But understanding and reproducing softness perception is challenging, because it involves so many sensory and cognitive processes.

Softness Rendering Interface

Previous attempts have not distinguished between two primary elements of softness perception: cutaneous cues (sensory feedback from the skin of the fingertip), and kinesthetic cues (feedback about the amount of force on the finger joint).

Now researchers at the Reconfigurable Robotics Lab (RLL) in EPRL’s School of Engineering have done just that.

A softness interface

With SORI (Softness Rendering Interface), they were able to decouple cutaneous and kinesthetic cues for a range of real materials.

The research appears in the Proceedings of the National Academy of Science (PNAS).

We feel softness differently, dependng on skin contact

Neuroscientific and psychological studies show that cutaneous cues are largely based on how much skin is in contact with a surface. A surface that envelopes a greater area of your fingertip will be perceived as softer.

The reseachers developed parameters for the geometries of a fingertip and its contact surface to estimate the softness cues for that fingertip and extracted the softness parameters from a range of different materials. Then they mapped both sets of parameters onto the SORI device.

Creating softness for a range of materials

With this novel decoupling of kinesthetic and cutaneous functionality, SORI succeeded in recreating the softness of a range of materials—including beef, salmon, and marshmallow over the course of several experiments with two human volunteers.

It also mimicked materials with both soft and firm attributes (such as a biscuit on top of a marshmallow, and the sensation of a beating heart).

A wide range of robotic applications

Medicine is a primary area of potential application for this technology. For example, to train medical students to detect cancerous tumors, or to provide crucial sensory feedback to surgeons using robots to perform operations.

Other applications include robot-assisted exploration of space or the deep ocean, where the device could enable scientists to feel the softness of a discovered object from a remote location.

SORI is also a potential answer to one of the biggest challenges in robot-assisted agriculture: harvesting tender fruits and vegetables without crushing them.

Citation: Mete, M., Jeong, H., Wang, W. D., & Paik, J. (2024). SORI: A softness-rendering interface to unravel the nature of softness perception. Proceedings of the National Academy of Sciences, 121(13), e2314901121. https://doi.org/10.1073/pnas.2314901121

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The Illuminated Body

Researchers have invented a radical new wireless light source that might one day make it possible to “illuminate” the human body from the inside.

This minimally invasive design could allow for better understanding and treating diseases that currently require implanting bulky devices.

The new approach, described in the open-access journal Science Advances, was developed by a research team from the University of St Andrews and the University of Cologne in Europe.

Optical brain stimulation using wireless OLEDs

The design is based on integrating organic light-emitting diodes (OLEDs, commonly used on smartphones and televisions) on compact “acoustic antennas.”

Such optical stimulation techniques have emerged as a promising alternative to electrical stimulation because they can be more cell-selective, down to stimulating individual cells. This compares to conventional brain stimulators, which often incorporate a large number of electrodes.

Tech details

OLEDs consist of thin layers of organic materials that can be applied to almost any surface. The researchers exploit this property to deposit OLEDs directly onto the acoustic antenna, merging the unique properties of both platforms into a single extremely compact device.

In this way, acoustic antennas serve as substrate and power source for the custom-developed OLED. The antennas convert energy from a magnetic field into a mechanical oscillation and subsequently into an electric current by the “composite magnetoelectric effect.”

Wireless light source combines minimal device size, low operation frequency and optical stimulation

The new devices operate at sub-megahertz frequencies, a frequency range used for example for submarine communication, as electromagnetic fields at this frequency are only weakly absorbed by water. However, the intended application in biomedicine requires a small device in order to avoid a negative impact on the tissue.

“Many emerging applications require multiple sites to be stimulated independently, which is why modern brain stimulators often incorporate a large number of electrodes, said Humboldt Professor Dr Malte Gather, head of the Humboldt Centre for Nano- and Biophotonics at the Department of Chemistry of the University of Cologne’s Faculty of Mathematics and Natural Sciences.

No bulky electronics

“In the case of our wireless light sources, the devices can be independently controlled and operated without the need of additional and potentially bulky electronics.”

This is possible because the operation frequencies of different acoustic antennas can be tuned to different values. In the future, this could allow for the individual control of multiple stimulators in different parts of the body, to treat tremor in the late stages of Parkinson’s disease, for example.

Citation: Butscher, J. F., Hillebrandt, S., Mischok, A., Popczyk, A., H. Booth, J. H., & Gather, M. C. (2024). Wireless magnetoelectrically powered organic light-emitting diodes. Science Advances.

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New microscopy technique reveals activity of one million neurons across the mouse brain

One of the mysteries in neuroscience is how tools that capture relatively few components of brain activity have allowed scientists to predict behavior in mice, while much of the complexity of a mouse brain is “irrelevant background noise,” says Rockefeller University physicist Alipasha Vaziri.

In 2021, Vaziri’s lab developed light-beads microscopy (LBM), which enabled a 100-fold increase in the number of neurons that could be simultaneously recorded.

Recording one million neurons

The researchers have now recorded the activity of more than one million neurons across the entire cortex of the mouse brain for the first time. Animals were observed by multiple cameras from different angles.

The researchers used LBM in combination with advanced data analysis, computational modeling, and machine learning techniques to study the neural activity of mice as they spontaneously moved and reacted to their environment.

A brain observatory

LBM will be used in the Rockefeller Brain Observatory, a new initiative spearheaded by Vaziri to make pioneering, commercially unavailable instruments accessible to neuroscientists “that can do things that are otherwise impossible,” Vaziri says. 

Vaziri and his team are also helping researchers at several universities, including at Stanford University and UCL-London, to replicate LBM technology in their own neuroscience labs. The data they’ve amassed from the current study is also available for analysis by other researchers.

Citation: Jason Manle et al. Simultaneous, cortex-wide neuronal population dynamics reveal unbounded scaling of dimensionality with neuron number up to one million neurons. Neuron. March 06, 2024. https://doi.org/10.1016/j.neuron.2024.02.011

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Good news for sedentary workers

Increasing your daily step count may counteract the health consequences of too much sedentary time each day, a new study of more than 72,000 people by the University of Sydney’s Charles Perkins Centre (Australia) has found.

Optimal number of steps per day

Every additional step, up to around 10,000 steps a day, was linked to reduced risk of death (39 percent) and cardiovascular disease (21 percent), regardless of how much remaining time was spent sedentary, the study found.

Published in the British Journal of Sports Medicine, the research is the first to objectively measure, via wrist-worn wearables, whether daily steps could offset the health risks of high sedentary behavior.

After taking account of other potential influences, the researchers calculated that the optimal number of steps per day to counteract high sedentary time was between 9000 to 10000, which lowered mortality risk by 39 percent and incident CVD risk by 21 percent. In both cases, 50 percent of the benefit was achieved at between 4000 and 4500 steps a day.

Research specifics

Researchers used data on 72,174 individuals (average age 61; 58% female) from the UK Biobank study—a major biomedical database—who had worn an accelerometer device on their wrist for seven days to measure their physical activity. The accelerometer data were used to estimate daily step count and time spent sedentary, that is sitting or lying down while awake.

The research team then followed the health trajectory of the participants by linking hospitalization data and death records.

Median steps and time

The median daily step count for participants was 6222 steps/day, and 2200 steps/day (the lowest 5 percent of daily steps among all participants) was taken as the comparator for assessing the impact on death and CVD events of increasing step count.

The median time spent sedentary was 10.6 hours/day, so study participants sedentary for 10.5 hours/day or more were considered to have high sedentary time while those who spent less than 10.5 hours/day sedentary were classified as low sedentary time.

Adjustments were made to eliminate biases, such as excluding participants with poor health, who were underweight or had a health event within two years of followup. Researchers also took into account factors such as age, sex, ethnicity, education, smoking status, alcohol consumption, diet and parental history of CVD and cancer.

Citation: Ahmadi MN, Rezende LFM, Ferrari G, et al. March 5, 2024. Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels? A device-based cohort study. British Journal of Sports Medicine Published Online First: doi: 10.1136/bjsports-2023-107221 (open access)

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