R3ds3rpent - Kode, Transistors And Spirit

Could we truly be blind to race?

Professor Osagie K. Obasogie’s research on how blind people perceive race reveals that understanding race is not simply based on visual cues, but based how we’re socialized and what we’re taught.

When asked what race a person was, the respondents who were all blind at birth, largely defined race by visually observable indicators, such as skin color, facial features and other physical characteristics.

And contrary to what many might think, the UC Hastings professor found that blind people don’t rely on audible cues as a way to identify a person’s race, because many of them have learned that speech is an unreliable marker of someone’s race.

Instead, Obasogie’s subjects understood race visually based on the physical traits that they were taught to be markers for racial differences.

In the study, subjects recalled childhood experiences where they were told what people of certain color look like or even smell like.

And people around them often reinforced racial biases by patrolling racial boundaries, such as telling them they can’t date outside their race, or implying that the person next to them could be potentially dangerous. Obasogie told NPR:

“Blind people aren’t any more or less racist than anyone else. Indeed, part of the point of this project is that vision has very little to do with it. What matters are the social practices that train us to see and experience race in certain ways, regardless of whether we are sighted or not.”

Read more about Obasogie’s study at Boston Globe

And thank you to Julia Wilde at “That’s So Science” for hosting the DNews episode!

The Cosmic Web: Large structures in the universe on the scale of billions of light years

via reddit

Helping Hand

Robots, video games, and a radical new approach to treating stroke patients.

BY KAREN RUSSELL

In late October, when the Apple TV was relaunched, Bandit’s Shark Showdown was among the first apps designed for the platform. The game stars a young dolphin with anime-huge eyes, who battles hammerhead sharks with bolts of ruby light. There is a thrilling realism to the undulance of the sea: each movement a player makes in its midnight-blue canyons unleashes a web of fluming consequences. Bandit’s tail is whiplash-fast, and the sharks’ shadows glide smoothly over rocks. Every shark, fish, and dolphin is rigged with an invisible skeleton, their cartoonish looks belied by the programming that drives them—coding deeply informed by the neurobiology of action. The game’s design seems suspiciously sophisticated when compared with that of apps like Candy Crush Soda Saga and Dude Perfect 2.

Bandit’s Shark Showdown’s creators, Omar Ahmad, Kat McNally, and Promit Roy, work for the Johns Hopkins School of Medicine, and made the game in conjunction with a neuroscientist and neurologist, John Krakauer, who is trying to radically change the way we approach stroke rehabilitation. Ahmad told me that their group has two ambitions: to create a successful commercial game and to build “artistic technologies to help heal John’s patients.” A sister version of the game is currently being played by stroke patients with impaired arms. Using a robotic sling, patients learn to sync the movements of their arms to the leaping, diving dolphin; that motoric empathy, Krakauer hopes, will keep patients engaged in the immersive world of the game for hours, contracting their real muscles to move the virtual dolphin.

Many scientists co-opt existing technologies, like the Nintendo Wii or the Microsoft Kinect, for research purposes. But the dolphin simulation was built in-house at Johns Hopkins, and has lived simultaneously in the commercial and the medical worlds since its inception. “We depend on user feedback to improve the game for John’s stroke patients,” Ahmad said. “This can’t work without an iterative loop between the market and the hospital.”

In December, 2010, Krakauer arrived at Johns Hopkins. His space, a few doors from the Moore Clinic, an early leader in the treatment of AIDS, had been set up in the traditional way—a wet lab, with sinks and ventilation hoods. The research done in neurology departments is, typically, benchwork: “test tubes, cells, and mice,” as one scientist described it. But Krakauer, who studies the brain mechanisms that control our arm movements, uses human subjects. “You can learn a lot about the brain without imaging it, lesioning it, or recording it,” Krakauer told me. His simple, non-invasive experiments are designed to produce new insights into how the brain learns to control the body. “We think of behavior as being the fundamental unit of study, not the brain’s circuitry. You need to study the former very carefully so that you can even begin to interpret the latter.”

Krakauer wanted to expand the scope of the lab, arguing that the study of the brain should be done in collaboration with people rarely found on a medical campus: “Pixar-grade” designers, engineers, computer programmers, and artists. Shortly after Krakauer arrived, he founded the Brain, Learning, Animation, Movement lab, or BLAM! That provocative acronym is true to the spirit of the lab, whose goal is to break down boundaries between the “ordinarily siloed worlds of art, science, and industry,” Krakauer told me. He believes in “propinquity,” the ricochet of bright minds in a constrained space. He wanted to create a kind of “neuro Bell Labs,” where different kinds of experts would unite around a shared interest in movement. Bell Labs is arguably the most successful research laboratory of all time; it has produced eight Nobel Prizes, and inventions ranging from radio astronomy to Unix and the laser. Like Bell,BLAM! would pioneer both biomedical technologies and commercial products. By developing a “self-philanthropizing ecosystem,” Krakauer believed, his lab could gain some degree of autonomy from traditionally conservative funding structures, like the National Institutes of Health.

The first problem that BLAM! has addressed as a team is stroke rehabilitation. Eight hundred thousand people in the U.S. have strokes each year; it is the No. 1 cause of long-term disability. Most cases result from clots that stop blood from flowing to part of the brain, causing tissue to die. “Picture someone standing on a hose, and the patch of grass it watered dying almost immediately,” Steve Zeiler, a neurologist and a colleague of Krakauer’s, told me. Survivors generally suffer from hemiparesis, weakness on one side of the body. We are getting better at keeping people alive, but this means that millions of Americans are now living for years in what’s called “the chronic state” of stroke: their recovery has plateaued, their insurance has often stopped covering therapy, and they are left with a moderate to severe disability.

In 2010, Krakauer received a grant from the James S. McDonnell Foundation to conduct a series of studies exploring how patients recover in the first year after a stroke. He was already well established in the worlds of motor-control and stroke research. He had discovered that a patient’s recovery was closely linked to the degree of initial impairment, a “proportional recovery rule” that had a frightening implication: if you could use early measures of impairment to make accurate predictions about a patient’s recovery three months later, what did that say about conventional physical therapy? “It doesn’t reverse the impairment,” Krakauer said.

Nick Ward, a British stroke and neurorehabilitation specialist who also works on paretic arms, told me that the current model of rehabilitative therapy for the arm is “nihilistic.” A patient lucky enough to have good insurance typically receives an hour each per day of physical, occupational, and speech therapy in the weeks following a stroke. “The movement training we are delivering is occurring at such low doses that it has no discernible impact on impairment,” Krakauer told me. “The message to patients has been: ‘Listen, your arm is really bad, your arm isn’t going to get better, we’re not going to focus on your arm,’ ” Ward said. “It’s become accepted wisdom that the arm doesn’t do well. So why bother?”

Krakauer and his team are now engaged in a clinical trial that will test a new way of delivering rehabilitation, using robotics and the video game made by Ahmad, Roy, and McNally, who make up an “arts and engineering” group within the Department of Neurology. Krakauer hopes to significantly reduce patients’ impairment, and to demonstrate that the collaborative model of BLAM! is “the way to go” for the future study and treatment of brain disease.

Reza Shadmehr, a Johns Hopkins colleague and a leader in the field of human motor-control research, told me, “He’s trying to apply things that we have developed in basic science to actually help patients. And I know that’s what you’re supposed to do, but, by God, there are very few people who really do it.”

“You bank on your reputation, in the more conventional sense, to be allowed to take these risks,” Krakauer said. “I’m cashing in my chits to do something wild.”

In 1924, Charles Sherrington, one of the founders of modern neuroscience, said, “To move things is all that mankind can do; for such the sole executant is muscle, whether in whispering a syllable or in felling a forest.” For Sherrington, a human being was a human doing.

Yet the body often seems to go about its business without us. As a result, we may be tempted to underrate the “intelligence” of the motor system. There is a deep-seated tendency in our culture, Krakauer says, to dichotomize brains and brawn, cognition and movement. But he points out that even a movement as simple as reaching for a coffee cup requires an incredibly sophisticated set of computations. “Movement is the result of decisions, and the decisions you make are reflected in movements,” Krakauer told me.

Motor skills, like Stephen Curry’s jump shot, require the acquisition and manipulation of knowledge, just like those activities we deem to be headier pursuits, such as chess and astrophysics. “Working with one’s hands is working with one’s mind,” Krakauer said, but the distinction between skill and knowledge is an ancient bias that goes back to the Greeks, for whom techne, skill, was distinct from episteme, knowledge or science.

Keep reading

Odor Biomarker For Alzheimer’s: Urine Test Could Predict Disease Onset

A new study from the Monell Center, the U.S. Department of Agriculture (USDA), and collaborating institutions reports a uniquely identifiable odor signature from mouse models of Alzheimer’s disease. The odor signature appears in urine before significant development of Alzheimer-related brain pathology, suggesting that it may be possible to develop a non-invasive tool for early diagnosis of Alzheimer’s disease.

The research is in Scientific Reports. (full open access)

The American Commute by Alasdair Rae.

Model sheds light on purpose of inhibitory neurons

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory have developed a new computational model of a neural circuit in the brain, which could shed light on the biological role of inhibitory neurons — neurons that keep other neurons from firing.

The model describes a neural circuit consisting of an array of input neurons and an equivalent number of output neurons. The circuit performs what neuroscientists call a “winner-take-all” operation, in which signals from multiple input neurons induce a signal in just one output neuron.

Using the tools of theoretical computer science, the researchers prove that, within the context of their model, a certain configuration of inhibitory neurons provides the most efficient means of enacting a winner-take-all operation. Because the model makes empirical predictions about the behavior of inhibitory neurons in the brain, it offers a good example of the way in which computational analysis could aid neuroscience.

The researchers presented their results at the conference on Innovations in Theoretical Computer Science. Nancy Lynch, the NEC Professor of Software Science and Engineering at MIT, is the senior author on the paper. She’s joined by Merav Parter, a postdoc in her group, and Cameron Musco, an MIT graduate student in electrical engineering and computer science.

For years, Lynch’s group has studied communication and resource allocation in ad hoc networks — networks whose members are continually leaving and rejoining. But recently, the team has begun using the tools of network analysis to investigate biological phenomena.

“There’s a close correspondence between the behavior of networks of computers or other devices like mobile phones and that of biological systems,” Lynch says. “We’re trying to find problems that can benefit from this distributed-computing perspective, focusing on algorithms for which we can prove mathematical properties.”

Artificial neurology

In recent years, artificial neural networks — computer models roughly based on the structure of the brain — have been responsible for some of the most rapid improvement in artificial-intelligence systems, from speech transcription to face recognition software.

An artificial neural network consists of “nodes” that, like individual neurons, have limited information-processing power but are densely interconnected. Data are fed into the first layer of nodes. If the data received by a given node meet some threshold criterion — for instance, if it exceeds a particular value — the node “fires,” or sends signals along all of its outgoing connections.

Each of those outgoing connections, however, has an associated “weight,” which can augment or diminish a signal. Each node in the next layer of the network receives weighted signals from multiple nodes in the first layer; it adds them together, and again, if their sum exceeds some threshold, it fires. Its outgoing signals pass to the next layer, and so on.

In artificial-intelligence applications, a neural network is “trained” on sample data, constantly adjusting its weights and firing thresholds until the output of its final layer consistently represents the solution to some computational problem.

Biological plausibility

Lynch, Parter, and Musco made several modifications to this design to make it more biologically plausible. The first was the addition of inhibitory “neurons.” In a standard artificial neural network, the values of the weights on the connections are usually positive or capable of being either positive or negative. But in the brain, some neurons appear to play a purely inhibitory role, preventing other neurons from firing. The MIT researchers modeled those neurons as nodes whose connections have only negative weights.

Many artificial-intelligence applications also use “feed-forward” networks, in which signals pass through the network in only one direction, from the first layer, which receives input data, to the last layer, which provides the result of a computation. But connections in the brain are much more complex. Lynch, Parter, and Musco’s circuit thus includes feedback: Signals from the output neurons pass to the inhibitory neurons, whose output in turn passes back to the output neurons. The signaling of the output neurons also feeds back on itself, which proves essential to enacting the winner-take-all strategy.

Finally, the MIT researchers’ network is probabilistic. In a typical artificial neural net, if a node’s input values exceed some threshold, the node fires. But in the brain, increasing the strength of the signal traveling over an input neuron only increases the chances that an output neuron will fire. The same is true of the nodes in the researchers’ model. Again, this modification is crucial to enacting the winner-take-all strategy.

In the researchers’ model, the number of input and output neurons is fixed, and the execution of the winner-take-all computation is purely the work of a bank of auxiliary neurons. “We are trying to see the trade-off between the computational time to solve a given problem and the number of auxiliary neurons,” Parter explains. “We consider neurons to be a resource; we don’t want too spend much of it.”

Inhibition’s virtues

Parter and her colleagues were able to show that with only one inhibitory neuron, it’s impossible, in the context of their model, to enact the winner-take-all strategy. But two inhibitory neurons are sufficient. The trick is that one of the inhibitory neurons — which the researchers call a convergence neuron — sends a strong inhibitory signal if more than one output neuron is firing. The other inhibitory neuron — the stability neuron — sends a much weaker signal as long as any output neurons are firing.

The convergence neuron drives the circuit to select a single output neuron, at which point it stops firing; the stability neuron prevents a second output neuron from becoming active once the convergence neuron has been turned off. The self-feedback circuits from the output neurons enhance this effect. The longer an output neuron has been turned off, the more likely it is to remain off; the longer it’s been on, the more likely it is to remain on. Once a single output neuron has been selected, its self-feedback circuit ensures that it can overcome the inhibition of the stability neuron.

Without randomness, however, the circuit won’t converge to a single output neuron: Any setting of the inhibitory neurons’ weights will affect all the output neurons equally. “You need randomness to break the symmetry,” Parter explains.

The researchers were able to determine the minimum number of auxiliary neurons required to guarantee a particular convergence speed and the maximum convergence speed possible given a particular number of auxiliary neurons.

Adding more convergence neurons increases the convergence speed, but only up to a point. For instance, with 100 input neurons, two or three convergence neurons are all you need; adding a fourth doesn’t improve efficiency. And just one stability neuron is already optimal.

But perhaps more intriguingly, the researchers showed that including excitatory neurons — neurons that stimulate, rather than inhibit, other neurons’ firing — as well as inhibitory neurons among the auxiliary neurons cannot improve the efficiency of the circuit. Similarly, any arrangement of inhibitory neurons that doesn’t observe the distinction between convergence and stability neurons will be less efficient than one that does.

Assuming, then, that evolution tends to find efficient solutions to engineering problems, the model suggests both an answer to the question of why inhibitory neurons are found in the brain and a tantalizing question for empirical research: Do real inhibitory neurons exhibit the same division between convergence neurons and stability neurons?

“This computation of winner-take-all is quite a broad and useful motif that we see throughout the brain,” says Saket Navlakha, an assistant professor in the Integrative Biology Laboratory at the Salk Institute for Biological Studies. “In many sensory systems — for example, the olfactory system — it’s used to generate sparse codes.”

“There are many classes of inhibitory neurons that we’ve discovered, and a natural next step would be to see if some of these classes map on to the ones predicted in this study,” he adds.

“There’s a lot of work in neuroscience on computational models that take into account much more detail about not just inhibitory neurons but what proteins drive these neurons and so on,” says Ziv Bar-Joseph, a professor of computer science at Carnegie Mellon University. “Nancy is taking a global view of the network rather than looking at the specific details. In return she gets the ability to look at some larger-picture aspects. How many inhibitory neurons do you really need? Why do we have so few compared to the excitatory neurons? The unique aspect here is that this global-scale modeling gives you a much higher-level type of prediction.”

This map shows where 25 iconic movies were filmed in New York City

(Image caption: The prefrontal cortex connects to a very specific region of the brainstem (the PAG) through prefrontal cortical neurons: those labeled in purple directly project to the PAG and control our instinctive behaviours. Credit: EMBL/Livia Marrone)

Neural connection keeps instincts in check

From fighting the urge to hit someone to resisting the temptation to run off stage instead of giving that public speech, we are often confronted with situations where we have to curb our instincts. Scientists at EMBL have traced exactly which neuronal projections prevent social animals like us from acting out such impulses. The study, published online in Nature Neuroscience, could have implications for schizophrenia and mood disorders like depression.

“Instincts like fear and sex are important, but you don’t want to be acting on them all the time,” says Cornelius Gross, who led the work at EMBL. “We need to be able to dynamically control our instinctive behaviours, depending on the situation.”

The driver of our instincts is the brainstem – the region at the very base of your brain, just above the spinal cord. Scientists have known for some time that another brain region, the prefrontal cortex, plays a role in keeping those instincts in check (see background information down below). But exactly how the prefrontal cortex puts a break on the brainstem has remained unclear.

Now, Gross and colleagues have literally found the connection between prefrontal cortex and brainstem. The EMBL scientists teamed up with Tiago Branco’s lab at MRC LMB, and traced connections between neurons in a mouse brain. They discovered that the prefrontal cortex makes prominent connections directly to the brainstem.

Gross and colleagues went on to confirm that this physical connection was the brake that inhibits instinctive behaviour. They found that in mice that have been repeatedly defeated by another mouse – the murine equivalent to being bullied – this connection weakens, and the mice act more scared. The scientists found that they could elicit those same fearful behaviours in mice that had never been bullied, simply by using drugs to block the connection between prefrontal cortex and brainstem.

These findings provide an anatomical explanation for why it’s much easier to stop yourself from hitting someone than it is to stop yourself from feeling aggressive. The scientists found that the connection from the prefrontal cortex is to a very specific region of the brainstem, called the PAG, which is responsible for the acting out of our instincts. However, it doesn’t affect the hypothalamus, the region that controls feelings and emotions. So the prefrontal cortex keeps behaviour in check, but doesn’t affect the underlying instinctive feeling: it stops you from running off-stage, but doesn’t abate the butterflies in your stomach.

The work has implications for schizophrenia and mood disorders such as depression, which have been linked to problems with prefrontal cortex function and maturation.

“One fascinating implication we’re looking at now is that we know the pre-frontal cortex matures during adolescence. Kids are really bad at inhibiting their instincts; they don’t have this control,” says Gross, “so we’re trying to figure out how this inhibition comes about, especially as many mental illnesses like mood disorders are typically adult-onset.”

Have a great weekend!

In case you’re having a bad day.