Brain Develops Motor Memory For Prosthetics

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ScienceDaily (July 24, 2009) — "Practice makes perfect" is the maxim drummed into students struggling to learn a new motor skill - be it riding a bike or developing a killer backhand in tennis. Stunning new research now reveals that the brain can also achieve this motor memory with a prosthetic device, providing hope that physically disabled people can one day master control of artificial limbs with greater ease.

In this study, macaque monkeys using brain signals learned how to move a computer cursor to various targets. What the researchers learned was that the brain could develop a mental map of a solution to achieve the task with high proficiency, and that it adhered to that neural pattern without deviation, much like a driver sticks to a given route commuting to work.
The study, conducted by scientists at the University of California, Berkeley, addresses a fundamental question about whether the brain can establish a stable, neural map of a motor task to make control of an artificial limb more intuitive.
"When your own body performs motor tasks repeatedly, the movements become almost automatic," said study principal investigator Jose Carmena, a UC Berkeley assistant professor with joint appointments in the Department of Electrical Engineering and Computer Sciences, the Helen Wills Neuroscience Institute, and the Program in Cognitive Science. "The profound part of our study is that this is all happening with something that is not part of one's own body. We have demonstrated that the brain is able to form a motor memory to control a disembodied device in a way that mirrors how it controls its own body. That has never been shown before."
Researchers in the field of brain-machine interfaces, including Carmena, have made significant strides in recent years in the effort to improve the lives of people with physical disabilities. An April 2009 survey by the Christopher and Dana Reeve Foundation found that nearly 1.3 million people in the United States suffer from some form of paralysis caused by spinal cord injury. When other causes of restricted movement are considered, such as stroke, multiple sclerosis and cerebral palsy, the number of Americans affected jumps to 5.6 million, the survey found.
Already, researchers have demonstrated that rodents, non-human primates and humans are able to control robotic devices or computer cursors in real time using only brain signals. But what had not been clear before was whether such a skill had been consolidated as a motor memory. The new study suggests that the brain is capable of creating a stable, mental representation of a disembodied device so that it can be controlled with little effort.
To demonstrate this, Carmena and Karunesh Ganguly, a post-doctoral fellow in Carmena's laboratory, used a mathematical model, or "decoder," that remained static during the length of the study, and they paired it with a stable group of neurons in the brain. The decoder, analogous to a simplified spinal cord, translated the signals from the brain's motor cortex into movement of the cursor.
It took about four to five days of practice for the monkeys to master precise control of the cursor. Once they did, they completed the task easily and quickly for the next two weeks.
As the tasks were being completed, the researches were able to monitor the changes in activity of individual neurons involved in controlling the cursor. They could tell which cells were firing when the cursor moved in specific directions. The researchers noticed that when the animals became proficient at the task, the neural patterns involved in the "solution" stabilized.
"The solution adopted is what the brain returned to repeatedly," said Carmena.
That stability is one of three major features scientists associate with motor memory, and it is all too familiar to music teachers and athletic coaches who try to help their students "unlearn" improper form or techniques, as once a motor memory has been consolidated, it can be difficult to change.
Other characteristics of motor memory include the ability for it to be rapidly recalled upon demand and its resistance to interference when new skills are learned. All three elements were demonstrated in the UC Berkeley study.
In the weeks after they achieved proficiency, the primates exhibited rapid recall by immediately completing their learned task on the first try. "They did it from the get-go; there was no need to retrain them," said Carmena.
Real-life examples of resistance to interference, the third feature of motor memory, include people who return to an automatic transmission car after learning how to drive stick-shift. In the study, the researchers presented a new decoder - marked by a different colored cursor - two weeks after the monkeys showed mastery of the first decoder.
As the monkeys were mastering the new decoder, the researchers would suddenly switch back to the original decoder and saw that the monkeys could immediately perform the task without missing a beat. The monkeys could easily switch back and forth between the two decoders, showing a level of neural plasticity never before associated with the control of a prosthetic device, the researchers said.
"This is a study that says that maybe one day, we can really think of the ultimate neuroprosthetic device that humans can use to perform many different tasks in a more natural way," said Carmena.
Yet, the researchers acknowledged that prosthetic devices will not match what millions of years of evolution have accomplished to enable animal brains to control body movement. The complexity of wiring one's brain to properly control the body is made clear whenever one watches an infant's haphazard attempts to find its own hands and feet.
"Nevertheless, beyond its clinical applications, which are very clear, this line of research sheds light on how the brain assembles and organizes neurons, and how it forms a motor memory to control the prosthetic device," Carmena said. "These are important, fundamental questions about how the brain learns in general.
This study was supported by the Christopher and Dana Reeve Foundation, the American Heart Association and the American Stroke Association.
Journal reference:
Ganguly K, Carmena JM. Emergence of a Stable Cortical Map for Neuroprosthetic Control. PLoS Biol, 7(7):e1000153 DOI: 10.1371/journal.pbio.1000153
Adapted from materials provided by University of California - Berkeley.

Brain's Center For Perceiving 3-D Motion Is Identified

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ScienceDaily (July 21, 2009) — Ducking a punch or a thrown spear calls for the power of the human brain to process 3-D motion, and to perceive an object (whether it's offensive or not) moving in three dimensions is critical to survival. It also leads to a lot of fun at 3-D movies.

Neuroscientists have now pinpointed where and how the brain processes 3-D motion using specially developed computer displays and an fMRI (functional magnetic resonance imaging) machine to scan the brain.
They found, surprisingly, that 3-D motion processing occurs in an area in the brain—located just behind the left and right ears—long thought to only be responsible for processing two-dimensional motion (up, down, left and right).
This area, known simply as MT+, and its underlying neuron circuitry are so well studied that most scientists had concluded that 3-D motion must be processed elsewhere. Until now.
"Our research suggests that a large set of rich and important functions related to 3-D motion perception may have been previously overlooked in MT+," says Alexander Huk, assistant professor of neurobiology. "Given how much we already know about MT+, this research gives us strong clues about how the brain processes 3-D motion."
For the study, Huk and his colleagues had people watch 3-D visualizations while lying motionless for one or two hours in an MRI scanner fitted with a customized stereovision projection system.
The fMRI scans revealed that the MT+ area had intense neural activity when participants perceived objects (in this case, small dots) moving toward and away from their eyes. Colorized images of participants' brains show the MT+ area awash in bright blue.
The tests also revealed how the MT+ area processes 3-D motion: it simultaneously encodes two types of cues coming from moving objects.
There is a mismatch between what the left and right eyes see. This is called binocular disparity. (When you alternate between closing your left and right eye, objects appear to jump back and forth.)
For a moving object, the brain calculates the change in this mismatch over time.
Simultaneously, an object speeding directly toward the eyes will move across the left eye's retina from right to left and the right eye's retina from left to right.
"The brain is using both of these ways to add 3-D motion up," says Huk. "It's seeing a change in position over time, and it's seeing opposite motions falling on the two retinas."
That processing comes together in the MT+ area.
"Who cares if the tiger or the spear is going from side to side?" says Lawrence Cormack, associate professor of psychology. "The most important kind of motion you can see is something coming at you, and this critical process has been elusive to us. Now we are beginning to understand where it occurs in the brain."
Huk, Cormack, and post-doctoral research and lead author Bas Rokers published their findings in Nature Neuroscience online the week of July 7. They are members of the Institute for Neuroscience and Center for Perceptual Systems. The research was supported by a National Science Foundation CAREER Award to Huk.
Adapted from materials provided by University of Texas at Austin, via EurekAlert!, a service of AAAS.

Scientists discover why we never forget how to ride a bicycle

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(PhysOrg.com) -- You never forget how to ride a bicycle - and now a University of Aberdeen led team of neuroscientists has discovered why.

Their research, published this month in Nature Neuroscience, has identified a key nerve cell in the brain that controls the formation of memories for motor skills such as riding a bicycle, skiing or eating with chop sticks.
When one acquires a new skill like riding a bicycle, the cerebellum is the part of the brain needed to learn the co-ordinated movement.
The research team, which includes scientists from the Universities of Aberdeen, Rotterdam, London, Turin and New York, has been working to understand the connections between nerve cells in the cerebellum that enable learning.
They discovered that one particular type of nerve cell -the so called molecular layer interneuron - acts as a "gatekeeper", controlling the electrical signals that leave the cerebellum. Molecular layer interneurons transform the electrical signals into a language that can be laid down as a memory in other parts of the brain.
Dr Peer Wulff, who led the research in Aberdeen together with Prof. Bill Wisden at the University's Institute of Medical Sciences, said: "What we were interested in was finding out how memories are encoded in the brain. We found that there is a cell which structures the signal output from the cerebellum into a particular code that is engraved as memory for a newly learned motor skill. "
It could pave the way for advancements in prosthetic devices to mimic normal brain functions, which could benefit those who have suffered brain disorders, such as a stroke or multiple sclerosis.
Dr Wulff said: "To understand the way that the normal brain works and processes information helps the development of brain-computer interfaces as prosthetic devices to carry out the natural brain functions missing in patients who have suffered a stroke or have multiple sclerosis.
"Our results are very important for people interested in how the brain processes information and produces and stores memories. One day these findings could be applied to the building of prosthetic devices by other research teams."
Provided by University of Aberdeen (news : web)

Entirely New Way To Study Brain Function Developed

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ScienceDaily (July 16, 2009) — Scientists at Duke University and the University of North Carolina have devised a chemical technique that promises to allow neuroscientists to discover the function of any population of neurons in an animal brain, and provide clues to treating and preventing brain disease.

With the technique they describe in the journal Neuron online on July 15, scientists will be able to noninvasively activate entire populations of individual types of neurons within a brain structure.
"We have discovered a method in which systemic administration of an otherwise inert chemical to a mutant mouse selectively activates a single group of neurons," said James McNamara, M.D., chairman of the Duke Department of Neurobiology and co-senior author of the paper. "Elaborating on this method promises to let scientists engineer different kinds of mutant mice in which single groups of neurons will be activated by this chemical, so scientists can understand the behaviors mediated by each of these groups."
Right now, most scientists gain knowledge of brain function by correlating brain activity with certain behaviors; connecting a damaged brain area with an observed loss of function; or activating entire brain structures invasively and observing the resulting behavior.
Knowing what a particular type of neuron in a specific brain region does will help researchers find the root of certain diseases so they can be effectively treated, said McNamara, an expert in epilepsy. He pointed out that the human brain contains billions of neurons that are organized into thousands of distinct groups that need to be studied.
Four years ago, co-senior author Bryan Roth, M.D., Ph.D., and colleagues at UNC set out to create a cell receptor activated by an inert drug, but not by anything else. "Basically we wanted to create a chemical switch," said Roth, who is the Michael Hooker Distinguished Professor of Pharmacology at UNC-Chapel Hill.
"We wanted to put this switch into neurons so we could selectively turn them on to study the brain," said Roth, who was trained as a psychiatrist. "At the time, this idea was science fiction."
They used yeast genetics to evolve a specific receptor that could react with a specific chemical, because yeast quickly produces new generations. "If the theory of evolution were not true, this experiment would not have worked," Roth added.
The lab then worked to create a similar receptor in mice. In the initial attempt to create mice that expressed the receptor, the lab targeted receptor expression to neurons in the hippocampus and cortex of the brain. The receptor was designed to be activated by the drug clozapine-N-oxide (CNO), which has no other effects on the mice and no effects on normal neurons, those without the receptor.
Roth asked a student to inject the mice with CNO. They expected to register some type of change in neuronal activity, but were very surprised to see the mice have seizures. Suddenly, they had a model for studying epilepsy.
Roth immediately looked for epilepsy experts to collaborate with and contacted McNamara at Duke. Together they worked on this system that allowed them to regulate the activity of neurons in mice with CNO that was injected and able to cross the blood-brain barrier to access deep-brain neurons. With this model, the scientists were able to examine neuronal activity leading to seizures and activity that occurred during seizures.
This receptor was designed for experimental use with animals. "Based on what we learn from animal models of disease, we could get better target treatments for humans," said Georgia Alexander, Ph.D., a postdoctoral fellow in Duke Neurobiology and co-lead author. "The great thing about these drug-activated receptors is that they can be applied to study any disease state, not just epilepsy. With this, you could try to selectively activate other populations of neurons, in an animal model of Parkinson's disease, for example." Roth said that the technique is not limited to neurons and brains, and is being used to study other cells in the body as well.
Alexander said researchers now can ask which areas of the brain are most susceptible to and critical to seizure generation, "because we can use similar techniques to inactivate or silence neurons, too."
For example, some people with seizures have a portion of their temporal lobes removed from their brains. "Now we can ask, 'Is there a different part of the brain or population of neurons we could selectively silence that would be an even better way to treat epilepsy patients?'" Alexander said.
Other authors include Miguel A. Nicolelis of the Duke Department of Neurobiology; John Hartmann of the UNC School of Medicine; co-lead author Sarah C. Rogan, Blaine N. Armbruster, Ying Pei and John A. Allen of the UNC Department of Pharmacology; Sheryl S. Moy of the UNC Department of Psychiatry; Randal J. Nonneman of the Neurodevelopmental Disorders Research Center; and Atheir I. Abbas of the Department of Biochemistry at Case Western Reserve University.
This work was funded by the National Institutes of Health and the National Alliance for Research into Schizophrenia and Depression.
Adapted from materials provided by Duke University Medical Center.

Classifying 'Clicks' In African Languages To Clear Up 100-year-old Mystery

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ScienceDaily (July 16, 2009) — A new way to classify sounds in some human languages may solve a problem that has plagued linguists for nearly 100 years--how to accurately describe click sounds distinct to certain African languages.

Cornell University professor Amanda Miller and her colleagues recently used new high-speed, ultrasound imaging of the human tongue to precisely categorize sounds produced by the Nuu language speakers of southern Africa's Kalahari Desert. The research potentially could change how linguists describe "click languages" and help speech scientists understand the physics of speech production.
The African languages studied by Miller use a series of consonants called "clicks" which are unlike most consonants in that they are produced with air going into the mouth rather than out. The Nuu clicks, produced using both the front and back of the tongue, are difficult to characterize.
"When we say 'k' or 't,' the sound is produced by air breathing out of our lungs," said Miller. "But click sounds are produced by breathing in and creating suction within a cavity formed between the front and back parts of the tongue. While linguists knew this, most didn't want to accept it was something people controlled." So they loosely classified these click consonants using imprecise groupings.
"For nearly a century, some of these sounds fell into an imprecise catch-all category that included every type of modification ever reported in a click language," said Miller. "The movements of the tongue at the front of the mouth were quite accurately classified. But tongue movements at the back part of the mouth were not classified properly."
The reason was that prior tools were either too large to carry to fieldwork situations in Southern Africa, or too unsafe. Ultrasound imaging changed that by allowing Miller's research team to use safer, faster, non-invasive technology in the field to view the back part of the tongue.
Early ultrasound tools captured images only at about 30 frames per second, and thus are not able to keep up with the tongue's speed in fast sounds like clicks. The new ultrasound imaging tool is capable of capturing more than 125 frames per second, producing clearer images.
Miller and her colleagues used the high-speed ultrasound imaging to group the clicks more accurately. Her colleagues included Johanna Brugman, Cornell University; Bonny Sands, Northern Arizona University; Levi Namaseb, The University of Namibia; Mats Exter, University of Cologne; and Chris Collins, New York University.
"We wanted to classify clicks in the same way we classify other consonants," said Miller, who was a visiting faculty member at the University of British Columbia during the 2008-2009 academic year. "We think we've been pretty successful in doing that."
Nuu is severely endangered with fewer than 10 remaining speakers, all of whom are more than 60 years of age. Linguists are working diligently to document the unique aspects of this language before it disappears.
She explains her findings in the online version of the Journal of the International Phonetic Association posted on July 10. The National Science Foundation supports the research.
Adapted from materials provided by National Science Foundation.

Learning Is Both Social And Computational, Supported By Neural Systems Linking People

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ScienceDaily (July 16, 2009) — Education is on the cusp of a transformation because of recent scientific findings in neuroscience, psychology, and machine learning that are converging to create foundations for a new science of learning.

Writing in the July 17 edition of the journal Science, researchers report that this shift is being driven by three principles that are emerging from cross-disciplinary work: learning is computational, learning is social, and learning is supported by brain circuits linking perception and action that connect people to one another. This new science of learning, the researchers believe, may shed light into the origins of human intelligence.
"We are not left alone to understand the world like Robinson Crusoe was on his island," said Andrew Meltzoff, lead author of the paper and co-director of the University of Washington's Institute for Learning and Brain Sciences. "These principles support learning across the life span and are particularly important in explaining children's rapid learning in two unique domains of human intelligence, language and social understanding.
"Social interaction is more important than we previously thought and underpins early learning. Research has shown that humans learn best from other humans, and a large part of this is timing, sensitive timing between a parent or a tutor and the child," said Meltzoff, who is a developmental psychologist.
"We are trying to understand how the child's brain works – how computational abilities are changed in the presence of another person, and trying to use these three principles as leverage for learning and improving education," added co-author Patricia Kuhl, a neuroscientist and co-director of the UW's Institute for Learning and Brain Sciences.
University of California, San Diego robotics engineer Javier Movellan and neuroscientist-biologist Terrence Sejnowski are co-authors. The research was funded by the National Science Foundation and the National Institute of Child Health and Human Development. The National Science Foundation has funded large-scale science of learning centers at both universities.
The Science paper cites numerous recent advances in neuroscience, psychology, machine learning and education. For example, Kuhl said people don't realize how computational and social factors interact during learning.
"We have a computer between our shoulders and our brains are taking in statistics all the time without our knowing it. Babies learn simply by listening, for example. They learn the sounds and words of their language by picking up probabilistic information as they listen to us talk to them. Babies at 8 months are calculating statistically and learning," Kuhl said.
But there are limits. Kuhl's work has shown that babies gather statistics and learn when exposed to a second language face to face from a real person, but not when they view that person on television.
"A person can get more information by looking at another person face to face," she said. "We are digging to understand the social element and what does it mean about us and our evolution."
Apparently babies need other people to learn. They take in more information by looking at another person face to face than by looking at that person on a big plasma TV screen," she said. "We are now trying to understand why the brain works this way, and what it means about us and our evolution."
Meltzoff said an important component of human intelligence is that humans are built so they don't have to figure out everything by themselves.
"A major role we play as parents is teaching children where the important things are for them to learn," he said. "One way we do this is through joint visual attention or eye-gaze. This is a social mechanism and children can find what's important – we call them informational 'hot spots' – by following the gaze of another person. By being connected to others we also learn by example and imitation."
Infants, he said, learn by mixing self-discovery with observations of other people for problem-solving.
"We can learn what to do by watching others, and we also can come to understand other people through our own actions," Meltzoff said. "Learning is bi-directional."
The researchers believe that aspects of informal learning, the ways people, particularly children, learn outside school, need to be brought into the classroom.
"Educators know children spend 80 percent of their waking time away from school and children are learning deeply and enthusiastically in museums, in community centers, from online games and in all sorts of venues. A lot of this learning is highly social and clues from informal learning may be applied to school to enhance learning. Why is it that a kid who is so good at figuring out baseball batting averages is failing math in school?" said Meltzoff.
Even though it appears that babies do not learn from television, technology can play a big role in the science of learning. Research is showing that children are more receptive to learning from social robots, robots that are more human in appearance and more interactive.
"The more that interacting with a machine feels like interacting with a human, the more children – and maybe adults – learn," said Kuhl. "Someday we may understand how technology can help us learn a new language at any age, and, if we could, there are countless schools around the world in which that would be helpful."
"Science is trying to understand the magic of social interaction in human learning," said Meltzoff. "But when it does we hope to embody some of what we learn into technology. Kids today are using high-powered technology – Facebook, Twitter and text messaging – to enhance social interaction. Using technology, children are learning to solve problems collaboratively. Technology also allows us to have a distributed network from which to draw information, a world of knowledge."
Adapted from materials provided by University of Washington.

New Science Of Learning Offers Preview Of Tomorrow's Classroom

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ScienceDaily (July 16, 2009) — Of all the qualities that distinguish humans from other species, how we learn is one of the most significant. In the July 17, 2009 issue of the journal Science, researchers who are at the forefront of neuroscience, psychology, education, and machine learning have synthesized a new science of learning that is already reshaping how we think about learning and creating opportunities to re-imagine the classroom for the 21st century.

“To understand how children learn and improve our educational system, we need to understand what all of these fields can contribute,” explains Howard Hughes Medical Institute investigator Terrence J. Sejnowski, Ph.D., professor and head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and co-director of the Temporal Dynamics of Learning Center (TDLC) at the University of California, San Diego, which is sponsored by the National Science Foundation. “Our brains have evolved to learn and adapt to new environments; if we can create the right environment for a child, magic happens.”
The paper is the first major publication to emerge from a unique collaboration between the TDLC and the University of Washington’s Learning in Informal and Formal Environments (LIFE) Center. The TDLC focuses on the study of learning—from neurons to humans and robots—treating the element of time as a crucial component of the learning process. This work complements the psychological research on child development that is the principal focus of the LIFE Center. Both have been funded as part of the NSF’s Science of Learning initiative.
Among the key insights that the authors highlight are three principles to guide the study of human learning across a range of areas and ages: learning is computational— machine learning provides a unique framework to understand the computational skills that infants and young children possess that allow them to infer structured models of their environment; learning is social—a finding that is supported by studies showing that the extent to which children interact with and learn from a robot depends on how social and responsive its behavior is; and learning is supported by brain circuits linking perception and action— human learning is grounded in the incredibly complex brain machinery that supports perception and action and that requires continuous adaptation and plasticity.
As the only species to engage in organized learning such as schools and tutoring, homo sapiens also draw on three uniquely human social skills that are fundamental to how we learn and develop: imitation, which accelerates learning and multiplies learning opportunities; shared attention, which facilitates social learning; and empathy and social emotions, which are critical to understanding human intelligence and appear to be present even in prelinguistic children.
These and other advances in our understanding of learning are now contributing to the development of machines that are themselves capable of learning and, more significantly, of teaching. Already these “social robots,” which interface with humans through dialogue or other forms of communication and behave in ways that humans are comfortable with, are being used on an experimental basis as surrogate teachers, helping preschool-age children master basic skills such as the names of the colors, new vocabulary, and singing simple songs (see image).
“Social interaction is key to everything,” Sejnowski says. “The technology to merge the social with the instructional is out there, but it hasn’t been brought to bear on the classroom to create a personalized, individualized environment for each student.” He foresees a time when these social robots may offer personalized pedagogy tailored to the needs of each child and help track the student’s mastery of curriculum. “By developing a very sophisticated computational model of a child’s mind we can help improve that child’s performance.”
“For this new science to have an impact it is critical that researchers and engineers embed themselves in educational environments for sustained periods of time,” says coauthor Javier Movellan, Ph.D., co-PI of TDLC’s Social Interaction Network and director of the Machine Perception Laboratory at UC San Diego. “The old approach of scientists doing laboratory experiments and telling teachers what to do will simply not work. Scientists and engineers have a great deal to learn from educators and from daily life in the classroom.” Movellan is collaborating with teachers at the UC San Diego Early Childhood Education Center to develop social robots that assist teachers and create new learning opportunities for children.
What makes social interaction such a powerful catalyst for learning, how to embody key elements in technology to improve learning, and how to capitalize on social factors to teach children better and foster their innate curiosity remain central questions in the new science of learning.
“Our hope is that applying this new knowledge to learning will enhance educators’ ability to provide a much richer and more interesting intellectual and cultural life for everyone,” Sejnowski says.
Researchers who also contributed to this work include Andrew N. Meltzoff, D.Phil., and Patricia K. Kuhl, Ph.D., co-PI and PI, respectively, of the Learning in Informal and Formal Environments (LIFE) Center at the University of Washington
About the Temporal Dynamics of Learning Center
The Temporal Dynamics of Learning Center, in operation since 2006 as one of six Science of Learning centers across the country, is funded by the National Science Foundation.
The TDLC mission is to develop a new science of learning that treats time as a crucial component in the learning process, on time scales that range from milliseconds to years. There is also a particular focus on inreach from the classroom into the labs and translation of the science back into the classroom.
Adapted from materials provided by Salk Institute, via EurekAlert!, a service of AAAS.