Lifeboat Foundation

So many ways. But one crucial use is to help understand the basis of brain diseases. A reference human brain atlas that describes a normal or neurotypical brain could help researchers understand depression or schizophrenia or many other kinds of diseases, Lein says. Take Alzheimer’s as an example. You could apply these same methods to characterize the brains of people with differing levels of severity of Alzheimer’s, and then compare those brain maps with the reference atlas. “And now you can start to ask questions like, ‘Are certain kinds of cells vulnerable in disease, or are certain kinds of cells causal,” Lein says. (He’s part of a team that’s already working on this.) Rather than investigating plaques and tangles, researchers can ask questions about “very specific kinds of neurons that are the real circuit elements that are likely to be perturbed and have functional consequences,” he says.

What’s the next step?

Better resolution. “The next phase is really moving into very comprehensive coverage of the human and non-human primate brain in adults and development.” In fact, that work has already begun with the BRAIN Initiative Cell Atlas Network, a five-year, $500 million project. The aim is to generate a complete reference atlas of cell types in the human brain across the lifespan, and also to map cell interactions that underlie a wide range of brain disorders.

Australian researchers have designed an algorithm that can intercept a man-in-the-middle (MitM) cyberattack on an unmanned military robot and shut it down in seconds.

In an experiment using deep learning neural networks to simulate the behavior of the human brain, artificial intelligence experts from Charles Sturt University and the University of South Australia (UniSA) trained the robot’s operating system to learn the signature of a MitM eavesdropping cyberattack. This is where attackers interrupt an existing conversation or data transfer.

The algorithm, tested in real time on a replica of a United States army combat ground vehicle, was 99% successful in preventing a malicious attack. False positive rates of less than 2% validated the system, demonstrating its effectiveness.

Comcast customers can soon get upload and download speeds of up to 2 Gbps even if they don’t have a fiber connection at their home.

The ocean has always been a force to be reckoned with when it comes to understanding and traversing its seemingly limitless blue waters. Past innovations such as deep-sea submersibles and ocean-observing satellites have helped illuminate some wonders of the ocean though many questions still remain.

These questions are closer to being answered thanks to the development of the Intelligent Swift Ocean Observing System (ISOOS). Using this system, targeted regions of the ocean can be mapped in a three-dimensional method allowing for more data to be gathered in a safer, quicker and more efficient method than existing technologies can achieve.

Researchers published their results in Ocean-Land-Atmosphere Research.

A new magnum opus posits the existence of a hidden mathematical link akin to the connection between electricity and magnetism.

Building better batteries is a tough technical challenge. AI might be able to help.

Sissie Hsiao, the head of Google Bard and the Google Assistant, talks about why inside the company feels like “the beginning all over again.”

Forget the cloud.

Northwestern University engineers have developed a new nanoelectronic device that can perform accurate machine-learning classification tasks in the most energy-efficient manner yet. Using 100-fold less energy than current technologies, the device can crunch large amounts of data and perform artificial intelligence (AI) tasks in real time without beaming data to the cloud for analysis.

With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics.

Summary: Unveiling the neurological enigma of traumatic memory formation, researchers harnessed innovative optical and machine-learning methodologies to decode the brain’s neuronal networks engaged during trauma memory creation.

The team identified a neural population encoding fear memory, revealing the synchronous activation and crucial role of the dorsal part of the medial prefrontal cortex (dmPFC) in associative fear memory retrieval in mice.

Groundbreaking analytical approaches, including the ‘elastic net’ machine-learning algorithm, pinpointed specific neurons and their functional connectivity within the spatial and functional fear-memory neural network.