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Detailed brain growth atlas in mice offers insights into brain development

Brain growth and maturation doesn’t progress in a linear, stepwise fashion. Instead, it’s a dynamic, choreographed sequence that shifts in response to genetics and external stimuli like sight and sound. This is the first high-resolution growth chart to explain changes of key brain cell types in the developing mouse brain, led by a team at Penn State College of Medicine and the Allen Institute for Brain Science.

Using advanced imaging techniques, the researchers constructed a series of 3D atlases that are like time-lapsed maps of the brain during its first two weeks after birth, offering an unparalleled look at a critical period of brain development. It’s a powerful tool to understand healthy brain development and neurodevelopmental disorders, the researchers explained.

The study, published in Nature Communications, also detailed how regions of the brain change in volume and explained the shift in density of key cell types within them.

Artificial neurons replicate biological function for improved computer chips

Researchers at the USC Viterbi School of Engineering and School of Advanced Computing have developed artificial neurons that replicate the complex electrochemical behavior of biological brain cells.

The innovation, documented in Nature Electronics, is a leap forward in neuromorphic computing technology. The innovation will allow for a reduction of the chip size by orders of magnitude, will reduce its energy consumption by orders of magnitude, and could advance artificial general intelligence.

Unlike conventional digital processors or existing neuromorphic chips based on silicon technology that merely simulate neural activity, these physically embody or emulate the analog dynamics of their biological counterparts. Just as neurochemicals initiate brain activity, chemicals can be used to initiate computation in neuromorphic (brain-inspired) . By being a physical replication of the biological process, they differ from prior iterations of artificial neurons that were solely mathematical equations.

Chemotherapy nerve damage linked to immune cell stress pathway activation

Scientists at Wake Forest University School of Medicine, in collaboration with researchers at Weill Cornell Medicine, have made a breakthrough in understanding why many cancer patients develop nerve damage after chemotherapy. Their new study reveals that a stress response inside certain immune cells can trigger this debilitating side effect. This discovery could open the door to new ways to prevent or treat nerve damage in cancer patients.

The study appears in Science Translational Medicine.

Chemotherapy-induced is a common and often severe side effect of cancer treatment, especially with drugs like paclitaxel. It can cause tingling, numbness and pain in the hands and feet, sometimes forcing patients to stop life-saving treatment early. Up to half of all patients receiving chemotherapy may experience this condition, but until now, the exact cause has remained a mystery.

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.

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