Knowing a little about the local connections on flight maps and other networks can reveal a lot about a system’s global structure.
For over a hundred years, scientists have held the belief that our thoughts, feelings, and dreams are shaped by the way various brain regions interact via a vast network of trillions of cellular connections.
However, a recent study led by the team at Monash University’s Turner Institute for Brain and Mental Health has examined more than 10,000 distinct maps of human brain activity and discovered that the overall shape of an individual’s brain has a much more substantial impact on our cognitive processes, emotions, and behavior than its intricate neuronal connectivity.
The study, recently published in the prestigious journal, Nature draws together approaches from physics, neuroscience, and psychology to overturn the century-old paradigm emphasizing the importance of complex brain connectivity, instead identifying a previously unappreciated relationship between brain shape and activity.
A researcher has used the technique of chemical mapping to study the spiral arms of our home galaxy: the Milky Way. According to Keith Hawkins, assistant professor at The University of Texas at Austin, chemical cartography might help us better grasp the structure and evolution of our galaxy.
“Much like the early explorers, who created better and better maps of our world, we are now creating better and better maps of the Milky Way,” mentioned Hawkins in an official release.
NASA/JPL-Caltech.
Continue reading “Scientists use chemical mapping to study the spiraling arms of the Milky Way” »
New mapping effort, using spatial technology, reveals the organization of the human intestine at single-cell resolution for the first time.
During routine navigation in daily life, our brains use spatial mapping and memory to guide us from point A to point B. Just as routine: making a mistake in navigation that requires a course correction.
Now, researchers at Harvard Medical School have identified a specific group of neurons in a brain region involved in navigation that undergo bursts of activity when mice running a maze veer off course and correct their error.
The findings, published July 19 in Nature, bring scientists a step closer to understanding how navigation works, while raising new questions. These questions include the specific role these neurons play during navigation, and what they are doing in other brain regions where they are found.
Researchers from Carnegie Mellon University have developed a new technique that could lead to faster and more efficient drone exploration.
A team of researchers from Carnegie Mellon University has successfully developed a new dual-mapping technique that could help robots explore areas faster and more efficiently. By producing both a site’s high-and low-resolution map, this new technique enables robots to explore areas using only a fraction of the computing power typically needed for a similar task.
Continue reading “New dual-resolution technique opens door for faster drone exploration” »
During routine navigation in daily life, our brains use spatial mapping and memory to guide us from point A to point B. Just as routine is making a mistake in navigation that requires a course correction.
Now, researchers at Harvard Medical School have identified a specific group of neurons in a brain region involved in navigation that undergo bursts of activity when mice running a maze veer off course and correct their error.
Companion robots enhanced with artificial intelligence may one day help alleviate the loneliness epidemic, suggests a new report from researchers at Auckland, Duke, and Cornell Universities.
Their report, appearing in the July 12 issue of Science Robotics, maps some of the ethical considerations for governments, policy makers, technologists, and clinicians, and urges stakeholders to come together to rapidly develop guidelines for trust, agency, engagement, and real-world efficacy.
It also proposes a new way to measure whether a companion robot is helping someone.
Reservoir computing is a promising computational framework based on recurrent neural networks (RNNs), which essentially maps input data onto a high-dimensional computational space, keeping some parameters of artificial neural networks (ANNs) fixed while updating others. This framework could help to improve the performance of machine learning algorithms, while also reducing the amount of data required to adequately train them.
RNNs essentially leverage recurrent connections between their different processing units to process sequential data and make accurate predictions. While RNNs have been found to perform well on numerous tasks, optimizing their performance by identifying parameters that are most relevant to the task they will be tackling can be challenging and time-consuming.
Jason Kim and Dani S. Bassett, two researchers at University of Pennsylvania, recently introduced an alternative approach to design and program RNN-based reservoir computers, which is inspired by how programming languages work on computer hardware. This approach, published in Nature Machine Intelligence, can identify the appropriate parameters for a given network, programming its computations to optimize its performance on target problems.
