Lifeboat Foundation

A matter-wave interferometer can probe the magnetism of a broad range of species, from single atoms to very large, weakly magnetic molecules.

This year marks the centenary of the ground-breaking experiment of Otto Stern and Walther Gerlach that demonstrated the quantization of the spin angular momentum of an atom [1]. The evidence came from the observation that a beam of silver atoms, upon traversing a spatially varying magnetic field, split into two beams. The spatial splitting of the spin-up and spin-down atoms corresponded to an atomic magnetic moment of 1 Bohr magneton—the magnetic moment of a single spinning electron. The deflection of particle beams in a spatially varying magnetic field remains the basis of techniques for characterizing the magnetic properties of isolated atoms and molecules. Such techniques, however, aren’t sufficiently sensitive to study very large, weakly magnetic molecules, including many biological molecules.

Predictive models that relate brain activity to phenotype reliably fail when applied to subgroups of participants who do not fit stereotypical profiles, showing that the utility of a one-size-fits-all modelling approach is limited.

Understanding how the brain organizes and accesses spatial information — where we are, what’s around the corner, how to get there — remains an exquisite challenge. The process involves recalling an entire network of memories and stored spatial data from tens of billions of neurons, each connected to thousands of others. Neuroscientists have identified key elements such as grid cells, neurons that map locations. But going deeper will prove tricky: It’s not as though researchers can remove and study slices of human gray matter to watch how location-based memories of images, sounds and smells flow through and connect to each other.

Artificial intelligence offers another way in. For years, neuroscientists have harnessed many types of neural networks — the engines that power most deep learning applications — to model the firing of neurons in the brain. In recent work, researchers have shown that the hippocampus, a structure of the brain critical to memory, is basically a special kind of neural net, known as a transformer, in disguise. Their new model tracks spatial information in a way that parallels the inner workings of the brain. They’ve seen remarkable success.

“The fact that we know these models of the brain are equivalent to the transformer means that our models perform much better and are easier to train,” said James Whittington, a cognitive neuroscientist who splits his time between Stanford University and the lab of Tim Behrens at the University of Oxford.

Collaboration will seek to advance the development of a quantum internet.

https://www.youtube.com/watch?v=9UM8kpnLSNw

In this age of innovation and technology, Humanoid robots working closely.
with actual humans, are used for research and space exploration, personal.
assistance and caregiving, education and entertainment, search and.
rescue, manufacturing and maintenance, public relations, and healthcare.

This is not a dream or the distant future but current reality!

Continue reading “10 Most Advanced Humanoid Robots” »

Bionic technology is removing physical barriers faced by disabled people while raising profound questions of what it is to be human. From DIY prosthetics realised through 3D printing technology to customised AI-driven limbs, science is at the forefront of many life-enhancing innovations.

Support the Guardian ► https://support.theguardian.com/contribute.

Continue reading “Beyond bionics: how the future of prosthetics is redefining humanity” »

An astrophysicist at the University of Bologna and a neurosurgeon at the University of Verona compared the network of neuronal cells in the human brain with the cosmic network of galaxies… and surprising similarities emerged.

In their paper published in Frontiers in Physics, Franco Vazza (astrophysicist at the University of Bologna) and Alberto Feletti (neurosurgeon at the University of Verona) investigated the similarities between two of the most challenging and complex systems in nature: the cosmic network of galaxies and the network of neuronal cells in the human brain.

Despite the substantial difference in scale between the two networks (more than 27 orders of magnitude), their quantitative analysis, which sits at the crossroads of cosmology and neurosurgery, suggests that diverse physical processes can build structures characterized by similar levels of complexity and self-organization.

With the help of ESO’s Very Large Telescope (VLT), astronomers have found six galaxies lying around a supermassive black hole when the Universe was less than a billion years old. This is the first time such a close grouping has been seen so soon after the Big Bang and the finding helps us better understand how supermassive black holes, one of which exists at the centre of our Milky Way, formed and grew to their enormous sizes so quickly. It supports the theory that black holes can grow rapidly within large, web-like structures which contain plenty of gas to fuel them.

“This research was mainly driven by the desire to understand some of the most challenging astronomical objects —supermassive black holes in the early Universe. These are extreme systems and to date we have had no good explanation for their existence,” said Marco Mignoli, an astronomer at the National Institute for Astrophysics (INAF) in Bologna, Italy, and lead author of the new research published today in Astronomy & Astrophysics.

The new observations with ESO’s VLT revealed several galaxies surrounding a supermassive black hole, all lying in a cosmic “spider’s web” of gas extending to over 300 times the size of the Milky Way. “The cosmic web filaments are like spider’s web threads,” explains Mignoli. “The galaxies stand and grow where the filaments cross, and streams of gas—available to fuel both the galaxies and the central supermassive black hole—can flow along the filaments.”

Researchers have created a way for artificial neuronal networks to communicate with biological neuronal networks. The new system converts artificial electrical spiking signals to a visual pattern than is then used to entrain the real neurons via optogenetic stimulation of the network. This advance will be important for future neuroprosthetic devices that replace damages neurons with artificial neuronal circuitry.

A prosthesis is an artificial device that replaces an injured or missing part of the body. You can easily imagine a stereotypical pirate with a wooden leg or Luke Skywalker’s famous robotic hand. Less dramatically, think of old-school prosthetics like glasses and contact lenses that replace the natural lenses in our eyes. Now try to imagine a prosthesis that replaces part of a damaged brain. What could artificial brain matter be like? How would it even work?

Creating neuroprosthetic technology is the goal of an international team led by by the Ikerbasque Researcher Paolo Bonifazi from Biocruces Health Research Institute (Bilbao, Spain), and Timothée Levi from Institute of Industrial Science, The University of Tokyo and from IMS lab, University of Bordeaux. Although several types of artificial neurons have been developed, none have been truly practical for neuroprostheses. One of the biggest problems is that neurons in the brain communicate very precisely, but electrical output from the typical electrical neural network is unable to target specific neurons. To overcome this problem, the team converted the electrical signals to light. As Levi explains, “advances in optogenetic technology allowed us to precisely target neurons in a very small area of our biological neuronal network.”