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Category: mapping

When speaking out feels risky: New study maps hidden dynamics of self-censorship

In an era when social media blurs the line between public and private speech, how do people decide whether to speak their minds or stay silent?

A new study from researchers at Arizona State University and the University of Michigan, published in the Proceedings of the National Academy of Sciences, offers a groundbreaking look at the strategic trade-offs individuals make when facing the threat of punishment for dissent.

The work, co-authored by Professor Stephanie Forrest and Assistant Professor Joshua J. Daymude in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at ASU, and Robert Axelrod from the University of Michigan, introduces a to explain when people choose to express dissent or self-censor.

Functional ultrasound neuroimaging reveals mesoscopic organization of saccades in the lateral intraparietal area

An amazing paper (link:) where functional ultrasound imaging (fUSI) is used to explore how brain activity in the lateral intraparietal cortex (LIP) can predict visual saccades (eye movements) in two monkeys. An impressive array of computational analyses are used to extract insights from the imaged regions. Indeed, predictive models developed by the authors remained fairly stable over the course of up to 900 days! I happen to know two of the authors (Sumner L Norman and Mikhail Shapiro): congratulations to them and their colleagues on this excellent publication!

Our results demonstrate that PPC contains subregions tuned to different directions. These tuned voxels were predominately within LIP and grouped into contiguous mesoscopic subpopulations. Multiple subpopulations existed within a given coronal plane, i.e., there were multiple preferred directions in each plane. A rough topography exists where anterior LIP had more voxels tuned to contralateral downwards saccades and posterior LIP had more voxels tuned to contralateral upwards saccades. These populations remained stable across more than 100–900 days.

We observed large effect sizes with changes in CBV on the order of 10–30% from baseline activity (Fig. 3). This is much larger than observed with BOLD fMRI where the effect size was ~0.4–2% on similar saccade-based event-related tasks27,32. Our results support a growing evidence base that establishes fUSI as a sensitive neuroimaging technique for detecting mesoscopic functional activity in a diversity of model organisms, including pigeons, rats, mice, nonhuman primates, ferrets, and infant and adult humans23,24,25,33,34,35,36,37,38,39,40.

Several studies have reported a patchiness in direction selectivity with many neighboring neurons tuned to approximately the same direction followed by an abruption to a patch of a different preferred direction13,14,41. These results match very closely with the results observed in this study where we found clusters within LIP tightly tuned to one direction with differently tuned clusters in close proximity within a given plane. These results further emphasize the high spatial resolution of fUSI for functional mapping of neuronal activity. These results also closely match a previous study that used fUSI to identify the tonotopic mapping of the auditory cortex and inferior colliculus in awake ferrets where the authors found a functional resolution of 100 µm for voxel responsiveness and 300 µm for voxel frequency tuning34.

Topographical sparse mapping: A neuro-inspired sparse training framework for deep learning models

AI models have been expanding dramatically in size and the number of trainable parameters. This rapid growth has introduced many challenges, including increased computational costs and inefficiencies. Dynamic sparse training has emerged as a novel approach to address overparameterization and achieve energy-efficient artificial neural network (ANN) architectures. The highly efficient neuro-inspired sparse design remains underexplored compared to the significant focus on random topology searches. We propose the Topographical Sparse Mapping (TSM) method, inspired by the vertebrate visual system and convergent units. TSM introduces a sparse input layer for MLPs, significantly reducing the number of parameters.

Holographic optogenetics could enable faster brain mapping for new discoveries

Recent technological advances have opened new possibilities for neuroscience research, allowing researchers to map the brain’s structure and synaptic connectivity (i.e., the junctions via which neurons communicate with each other) with increasing precision.

Despite these developments, most widely employed methods to image synaptic connectivity are slow and fail to precisely record changes in the connections between in vivo, or in other words, while animals are awake and engaging in specific activities.

Two different research groups, one based at Columbia University and UC Berkeley, and the other at the Vision Institute of Sorbonne University in Paris, introduced a promising approach to study synapses in vivo. Their proposed mapping strategies, outlined in two Nature Neuroscience papers, combine holographic optogenetics, a method to selectively and precisely stimulate or silence specific neuron populations, with .

The In Situ Structure of T-Series T1 Reveals a Conserved Lambda-Like Tail Tip

It is estimated that over 60% of known tailed phages are siphophages, which are characterized by a long, flexible, and non-contractile tail. Nevertheless, entire high-resolution structures of siphophages remain scarce. Using cryo-EM, we resolved the structures of T-series siphophage T1, encompassing its head, connector complex, tail tube, and tail tip, at near-atomic resolution. The density maps enabled us to build the atomic models for the majority of T1 proteins. The T1 head comprises 415 copies of the major capsid protein gp47, arranged into an icosahedron with a triangulation number of seven, decorated with 80 homologous trimers and 60 heterotrimers along the threefold and quasi-threefold axes of the icosahedron. The T1 connector complex is composed of two dodecamers (a portal and an adaptor) and two hexamers (a stopper and a tail terminator).

Astronomers Create First 3D Map of an Exoplanet’s Atmosphere

“Eclipse mapping allows us to image exoplanets that we can’t see directly, because their host stars are too bright,” said Dr. Ryan Challener.

What can a 3D map of an exoplanet’s atmosphere teach astronomers about the planet’s formation, evolution, and composition? This is what a recent study published in Nature Astronomy hopes to address as a team of scientists presented a first-time 3D map of an exoplanet’s atmosphere based on temperature. This study has the potential to help scientists better understand the formation and evolution of exoplanet atmospheres while opening the doors for developing better methods of studying them.

For the study, the researchers used data obtained from NASA’s James Webb Space Telescope to develop a new method called 3D eclipse mapping on WASP-18b, which is located just over 400 light-years from Earth and whose radius is slightly more than Jupiter’s while have ten times its mass. WASP-18b is known as an “ultra-hot” Jupiter, as it orbits extremely close to its star at 0.02024 astronomical units (AU) while completing one orbit in only 0.9 days. For context, the planet Mercury orbits our Sun at 0.387 AU and completes one orbit in 88 days. WASP-18b is also tidally locked to its star like our Moon is tidally locked to Earth.

In the end, the researchers found that WASP-18b’s “dayside” features variations in temperature and chemical composition while also exhibiting a circular “hotspot” where the largest amount of starlight hits the atmosphere. Additionally, the team found this hotspot is surrounded by a colder “ring” closer to the limbs of the planet, or the outer edges where the shape of the planet is visible.

Imaging technique maps fleeting intermediates in hydrogen electrocatalysis

Electrocatalytic transformations not only require electrical energy—they also need a reliable middleman to spark the desired chemical reaction. Surface metal-hydrogen intermediates can effectively produce value-added chemicals and energy conversion, but, given their low concentration and fleeting lifespan, they are difficult to characterize or study in depth, especially at the nanoscale.

NVIDIA Now Working On Its Own Robotaxis

Support CleanTechnica’s work through a Substack subscription or on Stripe.

Because why not?

NVIDIA has actually been involved in the robotaxi world for years, providing different hardware needs to various automakers who have been automating more and more driving. For example, I just noticed that four years ago I wrote about AutoX robotaxis using NVIDIA Drive. NVIDIA also put out a blog post highlighting that “Cruise, Zoox, DiDi, Oxbotica, Pony.ai and AutoX [were] developing level 4/5 systems on NVIDIA’s autonomous vehicle platform.” It also acquired DeepMap at that time. “DeepMap expected to extend NVIDIA mapping products, scale worldwide map operations and expand NVIDIA’s full-self driving expertise,” the company announced in 2021.

Like radar, a brain wave sweeps a cortical region to read out information held in working memory

Imagine you are a security guard in one of those casino heist movies where your ability to recognize an emerging crime will depend on whether you notice a subtle change on one of the many security monitors arrayed on your desk. That’s a challenge of visual working memory.

According to a new study by neuroscientists in The Picower Institute for Learning and Memory at MIT, the ability to quickly spot the anomaly could depend on a theta-frequency brain wave (3–6 Hz) that scans through a region of the cortex that maps your field of view.

The findings in animals, published in Neuron, help to explain how the brain implements visual working memory and why performance is both limited and variable.

Astronomers expose CO-dark molecular gas, previously invisible to telescopes

An international team of astronomers has created the first-ever large-scale maps of a mysterious form of matter, known as CO-dark molecular gas, in one of our Milky Way’s most active star-forming neighborhoods, Cygnus X. Their findings, using the Green Bank Telescope (GBT), are providing crucial new clues about how stars formed in the Milky Way.

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