Lifeboat Foundation

A new analysis of Mars’ gravitational field has revealed hidden structures buried beneath the remains of an ancient ocean.

The work, which was presented this week at the Europlanetary Science Congress in Berlin, could add to a growing body of evidence that suggests the Red Planet may not be as geologically “dead,” or inactive, as once believed.

Overlaid with a thick and smooth layer of sediment which may have once been a seabed, the structures are significantly denser than their surroundings — though a more precise explanation of what they might be has so far eluded researchers.

One of the brain’s most celebrated qualities is its adaptability. Changes to neural circuits, whose connections are continually adjusted as we experience and interact with the world, are key to how we learn. But to keep knowledge and memories intact, some parts of the circuitry must be resistant to this constant change.

“Brains have figured out how to navigate this landscape of balancing between stability and flexibility, so that you can have new learning and you can have lifelong memory,” says neuroscientist Mark Harnett, an investigator at MIT’s McGovern Institute for Brain Research.

In research published in Cell Reports, Harnett and his team show how individual neurons can contribute to both parts of this vital duality. By studying the synapses through which pyramidal neurons in the brain’s sensory cortex communicate, they have learned how the cells preserve their understanding of some of the world’s most fundamental features, while also maintaining the flexibility they need to adapt to a changing world.

A flexible screen inspired in part by squid can store and display encrypted images like a computer—using magnetic fields rather than electronics. The research is reported in Advanced Materials by University of Michigan engineers.

“It’s one of the first times where mechanical materials use magnetic fields for system-level encryption, information processing and computing. And unlike some earlier mechanical computers, this device can wrap around your wrist,” said Joerg Lahann, the Wolfgang Pauli Collegiate Professor of Chemical Engineering and co-corresponding author of the study.

Continue reading “Inspired by squids and octopi, a new screen stores and displays encrypted images without electronics” »

Developing large-scale neural network models that mimic the brain’s activity is a major goal in the field of computational neuroscience. Existing models that accurately reproduce aspects of brain activity are notoriously complex, and fine-tuning model parameters often requires significant time, intuition, and expertise.

New published research from an interdisciplinary group of researchers primarily based at Carnegie Mellon University and the University of Pittsburgh presents a novel solution to mitigate some of these challenges. The machine learning-driven framework, Spiking Network Optimization using Population Statistics (SNOPS), can quickly and accurately customize models that reproduce activity to mimic what’s observed in the brain.

Continue reading “Novel framework allows for automated tuning of large-scale neuronal models” »

New research led by the Institute for Bioengineering of Catalonia (IBEC) has studied the migratory movement of groups of cells using light control. The results show that there is no leader cell that directs the collective movement, as previously thought, but that all cells participate in the process.

A team of researchers from the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) in Hamburg, Germany, and Brookhaven National Laboratory in the United States has demonstrated a new way to study disorder in superconductors using terahertz pulses of light.

In a published in the journal npj Computational Materials, Oak Ridge National Laboratory scientists developed a deep learning model—a type of artificial intelligence that mimics human brain function—to analyze high-speed videos of plasma plumes during a process called pulsed laser deposition, or PLD.

Researchers have introduced a solution to the problem of light-sheet fluorescence microscopy: novel illumination beams designed based on deep learning using a trainable phase mask. Their study eliminates the need for sophisticated optical design tools, allowing optimization to be directly applied to improve image contrast.

A research team has proposed a novel approach to accurately describe electron transfer mediated nonadiabatic dynamics of molecules at metal surfaces. Their works were published in Physical Review Letters.