Coming soon: Advanced brain monitoring “while subjects make natural movements, including head nodding, stretching, drinking and playing a ball game.”
Credit: University of Nottingham ___ This Brain Scanner Is Way Smaller Than fMRI but Somehow 1,000% Creepier (Gizmodo): “It may look like something befitting Halloween’s Michael Myers, but the device pictured above is actually a breakthrough in neuroscience—a portable, wearable brain scanner that can monitor neural.
In summary — “I am cautiously optimistic about the promise of tDCS; cognitive training paired with tDCS specifically could lead to improvements in attention and memory for people of all ages and make some huge changes in society. Maybe we could help to stave off cognitive decline in older adults or enhance cognitive skills, such as focus, in people such as airline pilots or soldiers, who need it the most. Still, I am happy to report that we have at least moved on from torpedo fish” smile
In 47 CE, Scribonius Largus, court physician to the Roman emperor Claudius, described in his Compositiones a method for treating chronic migraines: place torpedo fish on the scalps of patients to ease their pain with electric shocks. Largus was on the right path; our brains are comprised of electrical signals that influence how brain cells communicate with each other and in turn affect cognitive processes such as memory, emotion and attention.
The science of brain stimulation – altering electrical signals in the brain – has, needless to say, changed in the past 2,000 years. Today we have a handful of transcranial direct current stimulation (tDCS) devices that deliver constant, low current to specific regions of the brain through electrodes on the scalp, for users ranging from online video-gamers to professional athletes and people with depression. Yet cognitive neuroscientists are still working to understand just how much we can influence brain signals and improve cognition with these techniques.
Brain stimulation by tDCS is non-invasive and inexpensive. Some scientists think it increases the likelihood that neurons will fire, altering neural connections and potentially improving the cognitive skills associated with specific brain regions. Neural networks associated with attention control can be targeted to improve focus in people with attention deficit-hyperactivity disorder (ADHD). Or people who have a hard time remembering shopping lists and phone numbers might like to target brain areas associated with short-term (also known as working) memory in order to enhance this cognitive process. However, the effects of tDCS are inconclusive across a wide body of peer-reviewed studies, particularly after a single session. In fact, some experts question whether enough electrical stimulation from the technique is passing through the scalp into the brain to alter connections between brain cells at all.
Scientists found that neurons in mammalian brains were capable of producing photons of light, or “Biophotons”!
The photons, strangely enough, appear within the visible spectrum. They range from near-infrared through violet, or between 200 and 1,300 nanometers.
Scientists have an exciting suspicion that our brain’s neurons might be able to communicate through light. They suspect that our brain might have optical communication channels, but they have no idea what could be communicated.
Elon Musk’s neurotechnology startup Neuralink filed for permits to build an in-house machine shop and a biological testing laboratory for its facility in San Francisco last year.
The documentation on the company’s 2017 permits was retrieved by Gizmodo, which was able to access Neuralink’s public records. An excerpt of a letter submitted by Neuralink executive Jared Birchall on February 2017 to the city’s planning department gives some clues about the company’s plans for the facility’s proposed machine shop and animal testing lab.
A revolutionary new theory contradicts a fundamental assumption in neuroscience about how the brain learns. According to researchers at Bar-Ilan University in Israel led by Prof. Ido Kanter, the theory promises to transform our understanding of brain dysfunction and may lead to advanced, faster, deep-learning algorithms.
In a paper published on March 15, 2018, in the journal Science, Stanford researchers led by Dr. Dena Leeman showed that intracellular protein aggregates accumulate within the lysosomes of neural stem cells that were previously thought not to suffer from this problem [1].
Intracellular waste disposal 101
Dysfunctional proteins and organelles within a cell constitute intracellular waste that the cell needs to dispose of. To do so, the cell may avail itself of proteasomes and lysosomes. Proteasomes are protein complexes that, with the help of enzymes, break down other, unnecessary proteins into shorter amino acids that can then be recycled to build new, useful proteins. Proteasomes are found within the cell nucleus and in the cytosol—the aqueous solution in which everything in a cell floats. The discovery of proteasomes happened later than that of lysosomes, which, for a while, were thought to be the only cellular waste management systems.
Https://paper.li/e-1437691924#/
In the natural world, intelligence takes many forms. It could be a bat using echolocation to expertly navigate in the dark, or an octopus quickly adapting its behavior to survive in the deep ocean. Likewise, in the computer science world, multiple forms of artificial intelligence are emerging — different networks each trained to excel in a different task. And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain.
“The fundamental questions cognitive neuroscientists and computer scientists seek to answer are similar,” says Aude Oliva of MIT. “They have a complex system made of components — for one, it’s called neurons and for the other, it’s called units — and we are doing experiments to try to determine what those components calculate.”
In Oliva’s work, which she is presenting at the CNS symposium, neuroscientists are learning much about the role of contextual clues in human image recognition. By using “artificial neurons” — essentially lines of code, software — with neural network models, they can parse out the various elements that go into recognizing a specific place or object.
