Episode 5: Dark Matters from Science Bytes on Vimeo.

Dark matter makes up 85% of the mass of the universe and is responsible for its underlying structure. Yet it doesn’t emit or absorb light. We can only observe how it pulls and tugs on other things. But scientists at Stanford have pioneered new visualization methods, based on massive computer simulations, that allow them to see and study dark matter in ways that have never before been possible.

We live in a world where our eyes and ears are almost constantly bombarded with colors, shapes, textures and noises of all types. How exactly do our brains translate these sights and sounds into meaningful images and words? At the University of California, Berkeley, two groups of scientists are finding tantalizing new answers to this question. Their remarkable successes at reconstructing what our brains see and hear offer hope for future life-changing technologies.

This video is based on:

Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, a paper published by Shinji Nishimoto, Jack Gallant, and colleagues in the journal Current Biology.

Reconstructing Speech from Human Auditory Cortex, a paper published by Brian Pasley, Robert Knight and colleagues in the online journal PLoS Biology.

Influence of Context and Behavior on Stimulus Reconstruction From Neural Activity in Primary Auditory Cortex, a paper published by Shihab A. Shamma and colleagues in the journal Journal of Neurophysiology.

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Why do our bodies wear out as we grow old? Meet Charles Mobbs, a scientist at Mount Sinai School of Medicine in New York. By trying to answer this mysterious question, he and his team have found what could be a way to do something long thought impossible: reverse kidney damage caused by diabetes.

The following is a short statement by Dr. Charles Mobbs:

“Diabetic complications, including kidney failure, develop progressively and become apparently irreversible, even with complete correction of blood glucose. However, based on our studies of the basic mechanisms driving diabetic complications, we hypothesized that decreasing glucose metabolism as well as blood glucose levels would allow reversal of diabetic complications. We had previously shown that a particular chemical known as a ketone, which is used as fuel for the brain during nutritional deprivation, could block glucose metabolism. Ketone levels in the blood can also be produced by the low-carbohydrate low-protein ketogenic diet, which is used to treat epilepsy in people.

We therefore tested if the ketogenic diet would reverse diabetic kidney disease. To do this we studied mice with either Type 1 or Type 2 diabetes, waited until they showed evidence of kidney disease (protein in the urine), then placed them on the ketogenic diet. Within days their blood glucose levels were completely corrected. After 8 weeks urine protein was completely corrected. The diet also corrected many molecular abnormalities caused by diabetic kidney disease. These studies show for the first time that diabetic kidney failure can be reversed by a relatively simple dietary manipulation. However, more studies on the safety and efficacy of this diet are required before the diet can be recommended for patients with diabetic kidney failure.”

This video is based on:

Reversal of Diabetic Nephropathy by a Ketogenic Diet, a paper published by Charles Mobbs and colleagues in the online journal PLoS ONE.

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When we touch something, how do sensations from our hands get translated into perceptions by our brains? Meet two scientists who are trying to answer that question with a curious tool: rat whiskers. Just like hands are to humans, whiskers are rats’ primary sensors of touch. Analyzing how whisker sensations get processed by rats’ brains is providing a powerful model that’s helping reveal the mysteries of our own sense of touch.

This video is based on:

The Morphology of the Rat Vibrissal Array: A Model for Quantifying Spatiotemporal Patterns of Whisker-Object Contact, a paper published by Mitra Hartmann and colleagues in the online journal PLoS Computational Biology.

Psychometric Curve and Behavioral Strategies for Whisker-Based Texture Discrimination in Rats, a paper published by Daniel Feldman and colleagues in the online journal PLoS ONE.

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Michael Schwarz, Executive Producer

How can three pounds of jelly inside our skulls enable us to do everything that makes us human? For centuries, scientists have been fascinated and puzzled by the mysterious workings of the brain. Now, for the first time, they can re-create in the computer the shapes of every one of the billions of nerve cells that make up our brains, the component parts of the intricate neural circuits that allow us to move, see and hear, to feel and to think. Armed with this new tool, scientists are beginning to decipher the secrets of the brain’s architecture, which may one day enable us to build smart technologies that surpass the capabilities of anything we have today.

This video is based on One Rule to Grow Them All: A General Theory of Neuronal Branching and Its Practical Application, a paper published by neuroscientist Hermann Cuntz and colleagues in the online journal PLoS Computational Biology.

Following is a statement by Dr. Hermann Cuntz:

The brain is composed of billions of cells called neurons. One neuron receives inputs from thousands of other neurons and sends out its signals to thousands more. We believe that if we understood the precise pattern with which neurons connect to each other, i.e. which neuron is connected with which other, we would understand how the brain works and how thoughts come about within the brain’s circuitry.

More than a hundred years ago, Ramón y Cajal’s big discovery was that you could read out the connection patterns between neurons by simply looking at their shapes: the tree-like input and output structures of neurons are not only beautiful but they are also optimized to connect to other neurons in an efficient manner.

Inspired by Cajal’s observations, we have designed a computer program that generates artificial shapes of neurons once the connection pattern is known. If we therefore are able to generate shapes that are indistinguishable from their real counterparts, we can conclude that we entered the correct connection patterns in order to generate the cells. We have then understood the connection pattern that led to these particular neuron shapes in the real brain.

Following is a statement by Dr. Eugene Izhikevich:

At Brain Corporation we’re working on the next generation of smart consumer products that will have what we call an artificial nervous system. The idea is that by creating mathematical models of the way the brain works, we can help build products that behave more like animals and less like robots.

These days we’re concentrating on creating large-scale models of the visual system that faithfully reproduce the biology of vision in animals. Some of these models depend on highly sophisticated algorithms that require enormous amounts of computer processing to generate millions of artificial neurons that have the same structures as real neurons.

To implement such large-scale models in a low-power mobile device, we’ve been collaborating with Qualcomm Incorporated (the largest mobile semiconductor company on the planet) to design new chips with massive computing power that will be the brains of our artificial nervous systems.
 

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