Lifeboat Foundation: Safeguarding Humanity
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Category: neuroscience

April 5, 2016, New York — People are using brain-machine interfaces to restore motor function in ways never before possible — through limb prosthetics and exoskletons. But technologies to repair and improve cognition have been more elusive. That is rapidly changing with new tools — from fully implantable brain devices to neuron-eavesdropping grids atop the brain — to directly probe the mind.

These new technologies, being presented today at the Cognitive Neuroscience Society (CNS) annual conference in New York City, are mapping new understandings of cognition and advancing efforts to improve memory and learning in patients with cognitive deficits.

Eavesdropping on neurons

“A new era” of electrophysiology is now upon us, says Josef Parvizi of Stanford University who is chairing the CNS symposium on the topic. “We have gotten a much sharper view of the brain’s electrophysiological activity” using techniques once relegated to science fiction.

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Scientists just got one step closer to understanding the nuts and bolts of how your mind handles emotions. An MIT team has identified two neural connections in the brain’s amygdala regions that process positive and negative emotional events. By tagging neuron groups with a light-sensitive protein, they discovered that the neurons form parallel but complex channels that respond differently to given situations. Some neurons within one of those connection will be excited by a feeling, while others will be inhibited — the combination of those reactions in a given channel may determine the emotion you experience.

It’s still early days. The researchers need to explore specific neuron populations in-depth to see how they’re connected, and they have to clearly define the larger neural circuits. If they succeed, though, they might help explain how mental health issues operate. Anxiety and depression might not fire the neurons that normally go off when you’re happy, for instance. The discoveries could lead to more effective treatments that restore your natural reactions.

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Preliminary work suggests that T-cells, which normally target disease, can be genetically engineered to target senescent cells in a wide range of tissues. In future, an infusion of GM blood every few years might be able to keep you going indefinitely (assuming some major advances in treating cancer, Alzheimer’s and heart disease). At which point, the question might be less: “How long have I got?” and more: “How long do you fancy sticking around?”

American scientists have coined the term ‘senolytics’ to describe a new class of drugs designed to delay the ageing process by clearing out doddery cells.

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In a video presented at IEEE Robotics and Automation Society’s annual conference, Chinese engineering students guide a living cockroach along S-shaped and Z-shaped paths using brain-to-brain interface: a bluetooth electroencephalogram (EEG) headset, translated and wirelessly sent to an electronic backpack receiver attached to the cockroach. The electrical impulses then stimulated the antennae nerves of the cockroach through a microelectrode implanted into its head. Watch the video released:

(Announced 16 June 2015 but only just came to our attention. And no, this is not April Fools post.)

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The machine overlords of the future may now, if it pleases them, eliminate all black and white imagery from the history of their meat-based former masters. All they’ll need is this system from Berkeley computer scientist Richard Zhang, which allows a soulless silicon sentience to “hallucinate” colors into any monochrome image.

It uses what’s called a convolutional neural network (several, actually) — a type of computer vision system that mimics low-level visual systems in our own brains in order to perceive patterns and categorize objects. Google’s DeepDream is probably the most well-known example of one. Trained by examining millions of images of— well, just about everything, Zhang’s system of CNNs recognizes things in black and white photos and colors them the way it thinks they ought to be.

Grass, for instance, has certain features — textures, common locations in images, certain other things often found on or near it. And grass is usually green, right? So when the network thinks it recognizes grass, it colors that region green. The same thing occurs for recognizing certain types of butterflies, building materials, flowers, the nose of a certain breed of dog and so on.

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Synthetic biology involves creating or re-engineering microbes or other organisms to perform specific tasks, like fighting obesity, monitoring chemical threats or creating biofuels. Essentially, biologists program single-celled organisms like bacteria and yeast much the same way one would program and control a robot.

But 10 years ago, it was extremely challenging to take a DNA sequence designed on a computer and turn it into a polymer that could implement its task in a specific host, say a mouse or human cell. Now, thanks to a multitude of innovations across computing, engineering, biology and other fields, researchers can type out any DNA sequence they want, email it to a synthesis company, and receive their completed DNA construct in a week. You can build entire chromosomes and entire genomes of bacteria in this way.

“Biology is the most powerful substrate for engineering that we know of,” said Christopher Voigt, Professor of Biological Engineering at MIT. “It’s more powerful than electrical engineering, mechanical engineering, materials science and others. Unlike all the other fields, we can look at what biology is already able to do. When we look at the natural world, we see things like the brain. That’s a complex place computing, electrical engineering and computer science can’t reach. The brain even constructs nanostructures very deliberately, something materials science has not accomplished.”

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Today, Lawrence Livermore National Lab (LLNL) and IBM announced the development of a new Scale-up Synaptic Supercomputer (NS16e) that highly integrates 16 TrueNorth Chips in a 4×4 array to deliver 16 million neurons and 256 million synapses. LLNL will also receive an end-to-end software ecosystem that consists of a simulator; a programming language; an integrated programming environment; a library of algorithms as well as applications; firmware; tools for composing neural networks for deep learning; a teaching curriculum; and cloud enablement.

The $1 million computer has 16 IBM microprocessors designed to mimic the way the brain works.

IBM says it will be five to seven years before TrueNorth sees widespread commercial use, but the Lawrence Livermore test is a big step in that direction.

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