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Actresses with passion in science

“People who never met her except across the footlights did not realize how, in her private life, she had such compassion and interest in everyone. After I returned from Hong Kong I was ill with a virus and she rang me up reproachfully later to say, ‘Why didn’t you let me know? I would have come and sit with you.’ Giving flowers to sick people is easy. Giving that precious commodity time is far more expensive for someone who had such a full life. But she always found time for everyone.” -Godfrey Winn

Making twisted semiconductors for 3-D projection

A smartphone display that can produce 3-D images will need to be able to twist the light it emits. Now, researchers at the University of Michigan and the Ben-Gurion University of the Negev have discovered a way to mass-produce spiral semiconductors that can do just that.

Back in 1962, University of Michigan engineers E. Leith and J. Upatnieks unveiled realistic 3-D images with the invention of practical holography. The first holographic images of bird on a train were made by creating standing waves of light with bright and dark spots in space, which creates an illusion of material object. It was made possible by controlling polarization and phase of light, i.e. the direction and the timing of electromagnetic wave fluctuations.

The semiconductor helices created by U-M-led team can do exactly that with photons that pass through, reflected from, and emitted by them. They can be incorporated into other semiconductor devices to vary the polarization, phase, and color of light emitted by the different pixels, each of them made from the precisely designed semiconductor helices.

Read more.

Balancing Time and Space in the Brain: A New Model Holds Promise for Predicting Brain Dynamics

For as long as scientists have been listening in on the activity of the brain, they have been trying to understand the source of its noisy, apparently random, activity. In the past 20 years, “balanced network theory” has emerged to explain this apparent randomness through a balance of excitation and inhibition in recurrently coupled networks of neurons. A team of scientists has extended the balanced model to provide deep and testable predictions linking brain circuits to brain activity.

Lead investigators at the University of Pittsburgh say the new model accurately explains experimental findings about the highly variable responses of neurons in the brains of living animals. On Oct. 31, their paper, “The spatial structure of correlated neuronal variability,” was published online by the journal Nature Neuroscience.

The new model provides a much richer understanding of how activity is coordinated between neurons in neural circuits. The model could be used in the future to discover neural “signatures” that predict brain activity associated with learning or disease, say the investigators.

“Normally, brain activity appears highly random and variable most of the time, which looks like a weird way to compute,” said Brent Doiron, associate professor of mathematics at Pitt, senior author on the paper, and a member of the University of Pittsburgh Brain Institute (UPBI). “To understand the mechanics of neural computation, you need to know how the dynamics of a neuronal network depends on the network’s architecture, and this latest research brings us significantly closer to achieving this goal.”

Earlier versions of the balanced network theory captured how the timing and frequency of inputs—excitatory and inhibitory—shaped the emergence of variability in neural behavior, but these models used shortcuts that were biologically unrealistic, according to Doiron.

“The original balanced model ignored the spatial dependence of wiring in the brain, but it has long been known that neuron pairs that are near one another have a higher likelihood of connecting than pairs that are separated by larger distances. Earlier models produced unrealistic behavior—either completely random activity that was unlike the brain or completely synchronized neural behavior, such as you would see in a deep seizure. You could produce nothing in between.”

In the context of this balance, neurons are in a constant state of tension. According to co-author Matthew Smith, assistant professor of ophthalmology at Pitt and a member of UPBI, “It’s like balancing on one foot on your toes. If there are small overcorrections, the result is big fluctuations in neural firing, or communication.”

The new model accounts for temporal and spatial characteristics of neural networks and the correlations in the activity between neurons—whether firing in one neuron is correlated with firing in another. The model is such a substantial improvement that the scientists could use it to predict the behavior of living neurons examined in the area of the brain that processes the visual world.

After developing the model, the scientists examined data from the living visual cortex and found that their model accurately predicted the behavior of neurons based on how far apart they were. The activity of nearby neuron pairs was strongly correlated. At an intermediate distance, pairs of neurons were anticorrelated (When one responded more, the other responded less.), and at greater distances still they were independent.

“This model will help us to better understand how the brain computes information because it’s a big step forward in describing how network structure determines network variability,” said Doiron. “Any serious theory of brain computation must take into account the noise in the code. A shift in neuronal variability accompanies important cognitive functions, such as attention and learning, as well as being a signature of devastating pathologies like Parkinson’s disease and epilepsy.”

While the scientists examined the visual cortex, they believe their model could be used to predict activity in other parts of the brain, such as areas that process auditory or olfactory cues, for example. And they believe that the model generalizes to the brains of all mammals. In fact, the team found that a neural signature predicted by their model appeared in the visual cortex of living mice studied by another team of investigators.

“A hallmark of the computational approach that Doiron and Smith are taking is that its goal is to infer general principles of brain function that can be broadly applied to many scenarios. Remarkably, we still don’t have things like the laws of gravity for understanding the brain, but this is an important step for providing good theories in neuroscience that will allow us to make sense of the explosion of new experimental data that can now be collected,” said Nathan Urban, associate director of UPBI.

Unusual Insects Taking Off

What do you do when you’re an insect researcher with a high-speed camera? Why, film all sorts of unusual insects from your backyard as they take off and fly! (Image and video credit: Ant Lab/A. Smith; via Colossal) Read the full article

Grading a slew of mediocre final papers, the grad student watches his months of arduous teaching bear little fruit.

Major Research Instrumentation Program

Credit: Photo by Lance Long; courtesy Electronic Visualization Laboratory, University of Illinois at Chicago

The Major Research Instrumentation program has helped to fund pieces of research equipment ranging from scanning probe microscopes, which have helped to visualize and characterize nano-scale biological tools, to nuclear magnetic resonance (NMR) spectrometers, which allow chemists to identify the individual molecules they make. Not only does this instrumentation help scientists advance their own research, it’s also used to train the next generation of scientists. For example, an X-ray diffractometer at Utah State University allowed Joan Hevel and Sean Johnson to teach four high school students in their lab about protein crystallization. Learn more.

The winners of the Miss Perfect Posture contest at a chiropractors convention, USA, 1956

via reddit

Rabies Viruses Reveal Wiring in Transparent Brains

Scientists under the leadership of the University of Bonn have harnessed rabies viruses for assessing the connectivity of nerve cell transplants. Coupled with a green fluorescent protein, the viruses show where replacement cells engrafted into mouse brains have connected to the host neural network.

The research is in Nature Communications. (full open access)