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Jan 26, 2019

Stable Perception in the Adult Brain

Posted by in categories: genetics, neuroscience

The adult brain has learned to calculate an image of its environment from sensory information. If the input signals change, however, even the adult brain is able to adapt − and, ideally, to return to its original activity patterns once the perturbation has ceased. Scientists at the Max Planck Institute of Neurobiology in Martinsried have now shown in mice that this ability is due to the properties of individual neurons. Their findings demonstrate that individual cells adjust strongly to changes in the environment but after the environment returns to its original state it is again the individual neurons which reassume their initial response properties. This could explain why despite substantial plasticity the perception in the adult brain is rather stable and why the brain does not have to continuously relearn everything.

Everything we know about our environment is based on calculations in our brain. Whereas a child’s brain first has to learn the rules that govern the environment, the adult brain knows what to expect and, for the most part, processes environmental stimuli in a stable manner. Yet even the adult brain is able to respond to changes, to form new memories and to learn. Research in recent years has shown that changes to the connections between neurons form the basis of this plasticity. But, how can the brain continually change its connections and learn new things without jeopardizing its stable representation of the environment? Neurobiologists in the Department of Tobias Bonhoeffer in Martinsried have now addressed this fundamental question and looked at the interplay between plasticity and stability.

The scientists studied the stability of the processing of sensations in the visual cortex of the mouse. It has been known for about 50 years that when one eye is temporarily closed, the region of the brain responsive to that eye increasingly becomes responsive to signals from the other eye that is still open. This insight has been important to optimize the use of eye patches in children with a squint. “Thanks to new genetically encoded indicators, it has recently become possible to observe reliably the activity of individual neurons over long periods of time,” says Tobias Rose, the lead author of the study. “With a few additional improvements, we were able to show for the first time what happens in the brain on the single-cell level when such environmental changes occur.”

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Jan 26, 2019

The Cause of Alzheimer’s Could Be Coming From Inside Your Mouth, Study Claims

Posted by in categories: biotech/medical, neuroscience

From the article:


In recent years, a growing number of scientific studies have backed an alarming hypothesis: Alzheimer’s disease isn’t just a disease, it’s an infection.

While the exact mechanisms of this infection are something researchers are still trying to isolate, a litany of papers argue the deadly spread of Alzheimer’s goes way beyond what we used to think.

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Jan 26, 2019

Big Pharma’s Drug Studies Are Getting a NASA-Style Makeover

Posted by in categories: biotech/medical, supercomputing, surveillance

Trying to streamline an operation that spends more than $5 billion a year on developing new drugs, Novartis dispatched teams to jetmaker Boeing Co. and Swissgrid AG, a power company, to observe how they use technology-laden crisis centers to prevent failures and blackouts. That led to the design of something that looks like the pharma version of NASA’s Mission Control: a global surveillance hub where supercomputers map and chart Novartis’s network of 500 drug studies in 70 countries, trying to predict potential problems on a minute-by-minute basis.


A third of development costs comes from clinical trials. Novartis wants to make them cheaper and faster.

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Jan 25, 2019

Our Neural Code: A Pathway to AI Minds?

Posted by in categories: neuroscience, quantum physics, robotics/AI

In May, 2016 I stumbled upon a highly controversial Aeon article titled “The Empty Brain: Your brain does not process information, retrieve knowledge or store memories. In short: your brain is not a computer” by psychologist Rob Epstein. This article attested to me once again just how wide the range of professional opinions may be when it comes to brain and mind in general. Unsurprisingly, the article drew an outrage from the reading audience. I myself disagree with the author on most fronts but one thing, I actually agree with him is that yes, our brains are not “digital computers.” They are, rather, neural networks where each neuron might function sort of like a quantum computer. The author has never offered his version of what human brains are like, but only criticized IT metaphors in his article. It’s my impression, that at the time of writing the psychologist hadn’t even come across such terms as neuromorphic computing, quantum computing, cognitive computing, deep learning, evolutionary computing, computational neuroscience, deep neural networks, and alike. All these IT concepts clearly indicate that today’s AI research and computer science derive their inspiration from human brain information processing — notably neuromorphic neural networks aspiring to incorporate quantum computing into AI cognitive architecture. Deep neural networks learn by doing just children.


By Alex Vikoulov.

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Jan 25, 2019

Tech Trends 2019

Posted by in categories: computing, neuroscience

For the tenth consecutive year, #Deloitte, a global leader in audit and consulting, lists the technological trends that will transform the processes, products, and services of the most innovative companies in the world this year.

These technologies include advanced network architectures, serverless computing, and intelligent interfaces, as well as increased development of digital, cognitive and cloud experiences.


Yes, uncertainty is disconcerting. But much of the tech-driven disruption today—and, likely, going forward—is both understandable and knowable.

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Jan 25, 2019

#OppyPhoneHome Update: We’re pulling out all the stops and trying new strategies to regain communication with Opportunity

Posted by in category: futurism

https://go.nasa.gov/2RR7KZ2

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Jan 25, 2019

Life’s good

Posted by in category: futurism

Oops sry wrong group…😁✨🕊.

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Jan 25, 2019

‘GO dough’ makes graphene easy to shape and mold

Posted by in category: materials

A Northwestern University team is reshaping the world of graphene—literally.

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Jan 25, 2019

Europe’s ‘New’ Periodic Table Predicts Which Elements Will Disappear in the Next 100 Years

Posted by in categories: chemistry, mobile phones

Scientists made a ‘new’ periodic table of elements to show how smartphones (and party balloons) are draining Earth’s resources.

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Jan 25, 2019

Quantum Computer: We’re Planning to Create One That Acts Like a Brain

Posted by in categories: quantum physics, robotics/AI

The human brain has amazing capabilities making it in many ways more powerful than the world’s most advanced computers. So it’s not surprising that engineers have long been trying to copy it. Today, artificial neural networks inspired by the structure of the brain are used to tackle some of the most difficult problems in artificial intelligence (AI). But this approach typically involves building software so information is processed in a similar way to the brain, rather than creating hardware that mimics neurons.

My colleagues and I instead hope to build the first dedicated neural network computer, using the latest “quantum” technology rather than AI software. By combining these two branches of computing, we hope to produce a breakthrough which leads to AI that operates at unprecedented speed, automatically making very complex decisions in a very short time.

We need much more advanced AI if we want it to help us create things like truly autonomous self-driving cars and systems for accurately managing the traffic flow of an entire city in real-time. Many attempts to build this kind of software involve writing code that mimics the way neurons in the human brain work and combining many of these artificial neurons into a network. Each neuron mimics a decision-making process by taking a number of input signals and processing them to give an output corresponding to either “yes” or “no”.

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