High-resolution maps of biological annotations in the brain are increasingly generated and shared. In this Review, Bazinet and colleagues discuss how brain connectomes can be enriched with biological annotations to address new questions about brain network organization.
In a suite of 21 papers published in the journals Science (12), Science Advances , and Science Translational Medicine , a large consortium of researchers shares new knowledge about the cells that make up our brains and the brains of other primates. It’s a huge leap from previously published work, with studies and data that reveal new insights about our nervous systems’ cellular makeup across many regions of the brain and what is distinctive about the human brain.
The research consortium is a concerted effort to understand the human brain and its modular, functional nature. It was brought together by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative.
Hundreds of scientists from around the world worked together to complete a range of studies exploring the cellular makeup of the human brain and those of other primates, and to demonstrate how a transformative new suite of scalable techniques can be used to study the detailed organization of the human brain at unprecedented resolution.
Google’s Green Light initiative uses AI and Google Maps to optimize traffic lights and reduce emissions.
Traffic jams are not only frustrating but also harmful to the environment. According to a study, road transportation accounts for a large share of global and urban greenhouse gas emissions, and the situation is worse at city intersections, where pollution can be 29 times higher than on open roads. The main reason is vehicles’ frequent stopping and starting, which consumes more fuel and emits more carbon dioxide.
But what if we could use artificial intelligence (AI) to optimize traffic lights and reduce these emissions? That is the idea behind Green Light, a Google Research initiative that… More.
Continue reading “Google’s Green Light: AI for smarter and greener traffic lights” »
Radiation mapping has evolved over the past decade, but there are still areas researchers would like to improve.
Scientists at the Berkeley Lab in the US are training a four-legged robot to detect and map radiation in any environment. This could revolutionize nuclear safety, security, and emergency response.
Thor Swift/Berkeley Lab.
Continue reading “Meet Spot, the robot dog that is helping map radiation” »
The anterior cingulate cortex (ACC) is believed to be involved in many cognitive processes, including linking goals to actions and tracking decision-relevant contextual information. ACC neurons robustly encode expected outcomes, but how this relates to putative functions of ACC remains unknown. Here, we approach this question from the perspective of population codes by analyzing neural spiking data in the ventral and dorsal banks of the ACC in two male monkeys trained to perform a stimulus-motor mapping task to earn rewards or avoid losses. We found that neural populations favor a low dimensional representational geometry that emphasizes the valence of potential outcomes while also facilitating the independent, abstract representation of multiple task-relevant variables. Valence encoding persisted throughout the trial, and realized outcomes were primarily encoded in a relative sense, such that cue valence acted as a context for outcome encoding. This suggests that the population coding we observe could be a mechanism that allows feedback to be interpreted in a context-dependent manner. Together, our results point to a prominent role for ACC in context setting and relative interpretation of outcomes, facilitated by abstract, or untangled, representations of task variables.
SIGNIFICANCE STATEMENT The ability to interpret events in light of the current context is a critical facet of higher-order cognition. The ACC is suggested to be important for tracking contextual information, whereas alternate views hold that its function is more related to the motor system and linking goals to appropriate actions. We evaluated these possibilities by analyzing geometric properties of neural population activity in monkey ACC when contexts were determined by the valence of potential outcomes and found that this information was represented as a dominant, abstract concept. Ensuing outcomes were then coded relative to these contexts, suggesting an important role for these representations in context-dependent evaluation. Such mechanisms may be critical for the abstract reasoning and generalization characteristic of biological intelligence.
This week saw the release of a treasure trove of data from the European Space Agency’s (ESA) Gaia mission, a space-based observatory that is mapping out the Milky Way in three dimensions. The newly released data includes half a million new stars and details about more than 150,000 asteroids within our solar system.
The overall aim of the Gaia mission is to create a full 3D map of our galaxy that includes not only stars, but also other objects like planets, comets, asteroids, and more. The mission was launched in 2013 and the data it collected is released in batches every few years, with previous releases including data on topics like the positions of over 1.8 billion stars.
The new data release fills in some gaps from previous releases, particularly in areas of the sky that are densely packed with stars — such as the Omega Centauri globular cluster, shown above. The new view of this cluster shows 10 times as many stars as the previous data, with a total of 526,587 new stars identified.
In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University’s Frontier Institute for Interdisciplinary Sciences has developed a technique called time-dependent stochastic parameter shift in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.
Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as “quantum space,” characterized by periodicity, and no need for endless subdivisions.
One way to illustrate this concept is by comparing the sizes of two elementary schools on a map. To do this, one might print out maps of the schools and then cut them into smaller pieces. After cutting, these pieces can be arranged into a line, with their total length compared (see Figure 1a). However, the pieces may not form a perfect rectangle, leading to inaccuracies. An infinite subdivision would be required to minimize these errors, an impractical solution, even for classical computers.
On October 10, the European Space Agency (ESA) published some interim data from its nearly a decade-long Gaia mission. The data includes half a million new and faint stars in a massive cluster, over 380 possible cosmic lenses, and the position of over 150,000 asteroids within the solar system.
[Related: See the stars from the Milky Way mapped as a dazzling rainbow.]
Launched in December 2013, Gaia is an astronomical observatory spacecraft with a mission to generate an accurate stellar census, thus mapping our galaxy and beyond. A more detailed picture of Earth’s place in the universe could help us better understand the diverse objects that make up the known universe.
Google Maps can now calculate rooftops’ solar potential, track air quality, and forecast pollen counts.
The platform recently launched a range of services like Solar API, which calculates weather patterns and pulls data from aerial imagery to help understand rooftops’ solar potential. The tool aims to help accelerate solar panel deployment by improving accuracy and reducing the number of site visits needed.
As seasonal allergies get worse every year, Pollen API shows updated information on the most common allergens in 65 countries by using a mix of machine learning and wind patterns. Similarly, Air Quality API provides detailed information on local air quality by utilizing data from multiple sources, like government monitoring stations, satellites, live traffic, and more, and can show areas affected by wildfires too.
Imagine you’re in an airplane with two pilots, one human and one computer. Both have their “hands” on the controllers, but they’re always looking out for different things. If they’re both paying attention to the same thing, the human gets to steer. But if the human gets distracted or misses something, the computer quickly takes over.
Meet the Air-Guardian, a system developed by researchers at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). As modern pilots grapple with an onslaught of information from multiple monitors, especially during critical moments, Air-Guardian acts as a proactive co-pilot; a partnership between human and machine, rooted in understanding attention.
But how does it determine attention, exactly? For humans, it uses eye-tracking, and for the neural system, it relies on something called “saliency maps,” which pinpoint where attention is directed. The maps serve as visual guides highlighting key regions within an image, aiding in grasping and deciphering the behavior of intricate algorithms. Air-Guardian identifies early signs of potential risks through these attention markers, instead of only intervening during safety breaches like traditional autopilot systems.
