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Sensory expectations configure neural responses before disturbances occur, study reveals

A study led by Jonathan Michaels, a Faculty of Health professor at York’s School of Kinesiology and Health Science, reveals how the brains of humans and monkeys use sensory expectations to prepare for unexpected disturbances, enabling faster and more accurate motor responses.

Published today in Nature, the study demonstrates that motor circuits across the brain do not passively wait for sensory signals. Instead, they proactively anticipate potential challenges, configuring themselves to respond effectively to disturbances. The research represents a significant leap forward in uncovering the brain’s predictive capabilities and its role in .

This advancement provides a clearer picture of the neural mechanisms underlying movement preparation and response, illustrating how expectation itself enhances precision and stability. The discovery opens new pathways for improving rehabilitation techniques and advancing brain-computer interface technology.

Common genetic causes across motor neuron diseases identified

Motor neuron diseases, such as amyotrophic lateral sclerosis (ALS) and hereditary spastic paraplegia (HSP), share physical similarities but have been largely viewed as genetically distinct. However, an analysis led by investigators from St. Jude Children’s Research Hospital and the University of Miami Miller School of Medicine discovered that there are previously unknown ultrarare gene variants (genetic changes found in extremely few individuals) linked to the diseases, and significant overlap of contributing genes between the diseases among patients without family histories of a motor neuron disease.

This new appreciation of the shared genetic origins of different motor neuron diseases is critical to deciphering the origins of these disorders and ultimately developing meaningful therapeutics. The findings are published in Translational Neurodegeneration.

While both ALS and HSP cause progressive motor dysfunction, the two disorders also have distinct characteristics. Weakness in ALS may begin in the arms, legs, head or neck. HSP, by contrast, begins in the legs. The causative, or “canonical” genes for these diseases are also largely distinct.

Neuromorphic computer prototype learns patterns with fewer computations than traditional AI

Could computers ever learn more like humans do, without relying on artificial intelligence (AI) systems that must undergo extremely expensive training?

Neuromorphic computing might be the answer. This emerging technology features brain-inspired computer hardware that could perform AI tasks much more efficiently with far fewer training computations using much less power than conventional systems. Consequently, neuromorphic computers also have the potential to reduce reliance on energy-intensive data centers and bring AI inference and learning to .

Dr. Joseph S. Friedman, associate professor of electrical and computer engineering at The University of Texas at Dallas, and his team of researchers in the NeuroSpinCompute Laboratory have taken an important step forward in building a neuromorphic computer by creating a small-scale prototype that learns patterns and makes predictions using fewer training computations than conventional AI systems. Their next challenge is to scale up the proof-of-concept to larger sizes.

Scalable approach to 6G wireless offers speed and reliability

A team from the University of California San Diego and Rensselaer Polytechnic Institute has invented a scalable technology that enables faster and more reliable 5G and 6G wireless communication.

“With our approach, we can support maybe 10 times more devices than before using the same bandwidth,” said Ish Kumar Jain, an assistant professor at Rensselaer Polytechnic Institute and alumnus of the UC San Diego Jacobs School of Engineering. “It also helps reduce latency (the delay in accessing the network) and maintains an extremely high data rate with all connected devices.”

The technique, dubbed FlexLink (patent pending), was co-developed by Dinesh Bharadia, associate professor with the Jacobs School of Engineering and affiliate of the Qualcomm Institute at UC San Diego, along with UC San Diego Ph.D. student Rohith Reddy Vennam.

Unit-free theorem pinpoints key variables for AI and physics models

Machine learning models are designed to take in data, to find patterns or relationships within those data, and to use what they have learned to make predictions or to create new content. The quality of those outputs depends not only on the details of a model’s inner workings but also, crucially, on the information that is fed into the model.

Some models follow a brute force approach, essentially adding every bit of data related to a particular problem into the model and seeing what comes out. But a sleeker, less energy-hungry way to approach a problem is to determine which variables are vital to the outcome and only provide the model with information about those key variables.

Now, Adrián Lozano-Durán, an associate professor of aerospace at Caltech and a visiting professor at MIT, and MIT graduate student Yuan Yuan, have developed a theorem that takes any number of possible variables and whittles them down, leaving only those that are most important. In the process, the model removes all units, such as meters and feet, from the underlying equations, making them dimensionless, something scientists require of equations that describe the physical world. The work can be applied not only to machine learning but to any .

Discovery of a new principle: Chiral molecules adhere to magnets

A research group at The University of Tokyo has discovered a new principle by which helical chiral molecules acquire spin through molecular vibrations, enabling them to adhere to magnets. Until now, it was believed that chiral molecules could only exhibit magnetic properties when an electric current was applied. This discovery overturns that conventional understanding.

Chiral molecules, which have a helical structure, are known to interact with magnets in a phenomenon known as chirality-induced spin selectivity (CISS). For instance, when a chiral molecule is connected to a magnet and an electric current is applied, magnetoresistance effects can be observed. It has also been reported that magnets can be used to separate right-handed and left-handed chiral molecules.

The prevailing explanation is that the flow of current through a chiral molecule induces magnetic properties, similar to an electromagnet. However, this explanation has limitations, as it does not fully account for the large magnetoresistance effects or CISS phenomena observed even in the absence of an electric current.

Interactive web tool brings quantum game theory concepts to life through music

A new interactive web application allows for a tangible understanding of abstract concepts of quantum game theory. The Kobe University development parallels the emergent dialog found in jazz and improvisational music and aims for a scientific exploration of creativity.

For many of us, , game theory and jazz are difficult concepts by themselves, and it is hard to imagine how they would combine. But Kobe University quantum engineer Souma Satofumi posits that not only can they fruitfully interact, but their combination also provides new avenues to understanding each of them.

Through creating the world’s first browser-based interactive music system based on quantum game theory, users are able to obtain visual and on how their respective strategies intertwine based on their inputs in what resembles a quantum jam session.

Bridging light, microwaves and electrons for precision calibration

EPFL researchers have developed a method to calibrate electron spectrometers with extreme accuracy by linking microwave, optical, and free-electron frequencies.

Frequency is one of the most precisely measurable quantities in science. Thanks to , tools that generate a series of equally spaced, precise frequencies like the teeth of a ruler, researchers can connect frequencies across the electromagnetic spectrum, from microwaves to optical light, enabling breakthroughs in timekeeping, spectroscopy, and navigation.

Electron energy-loss spectroscopy (EELS) is a powerful tool used to investigate the structure and properties of materials at the atomic level. It works by measuring how electrons lose energy as they pass through a sample. But although EELS provides excellent spatial resolution, its spectral resolution, the ability to measure energy precisely, has lagged behind optical methods.

A faster, more affordable way to produce quantum nanodiamonds holds promise for medicine and industry

An international team of scientists from three continents led by Dr. Petr Cígler of IOCB Prague has developed a method for creating light-emitting quantum centers in nanodiamonds in only a matter of minutes. In just one week, the process can yield as much material as conventional methods would produce in more than forty years.

Moreover, the resulting nanodiamonds show improved optical and quantum properties. The breakthrough brings us one step closer to the industrial production of higher-quality and more affordable quantum nanodiamonds, which have broad applications in research and technology. The article is published in Advanced Functional Materials.

The research team has introduced a new procedure called Pressure and Temperature Qubits (PTQ), which takes only four minutes. Diamond powder is placed in a press that generates extremely and temperature, reproducing the conditions found deep within Earth’s mantle. Under these conditions, quantum centers are formed inside the nanodiamonds.

Scientists Discover Ocean Bacteria That Feast on Plastic

A newly discovered enzyme motif reveals how ocean microbes are evolving to digest plastic, potentially aiding future cleanup efforts. Hidden in the depths of the ocean, scientists have discovered marine bacteria equipped with enzymes that can consume plastic, their evolution shaped by humanity’s

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