neurology


Robots Help Teach Social Skills to Kids with Autism Spectrum Disorder - News Center - The University of Texas at Dallas

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Now, a UT Dallas researcher is giving the fantasy of robotic friends a practical edge with a robot that teaches social skills to children with Autism Spectrum Disorder (ASD). "It's not to replace therapy with humans, but you can deliver a social skills lesson in a less threatening way, and the robot can deliver the same lesson multiple times," Rollins said. During a lesson, the robot explains a social situation to the child with ASD. Media Contact: Ben Porter, UT Dallas, (972) 883-2193, [email protected] or the Office of Media Relations, UT Dallas, (972) 883-2155, [email protected].


Robots Help Teach Social Skills to Kids with Autism Spectrum Disorder - News Center - The University of Texas at Dallas

#artificialintelligence

Now, a UT Dallas researcher is giving the fantasy of robotic friends a practical edge with a robot that teaches social skills to children with Autism Spectrum Disorder (ASD). "It's not to replace therapy with humans, but you can deliver a social skills lesson in a less threatening way, and the robot can deliver the same lesson multiple times," Rollins said. During a lesson, the robot explains a social situation to the child with ASD. Media Contact: Ben Porter, UT Dallas, (972) 883-2193, [email protected] or the Office of Media Relations, UT Dallas, (972) 883-2155, [email protected].


New AI system can decode your brain signals

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BERLIN: Scientists have developed a new artificial intelligence system that can decode brain signals, an advance that may help severely paralysed patients communicate with their thoughts. Researchers from University Hospital Freiburg in Germany led by neuroscientist Tonio Ball showed how a self-learning algorithm decodes human brain signals that were measured by an electroencephalogram (EEG). The system could be used for early detection of epileptic seizures, communicating with severely paralysed patients or make automatic neurological diagnosis. "Our software is based on brain-inspired models that have proven to be most helpful to decode various natural signals such as phonetic sounds," said Robin Tibor Schirrmeister, University Hospital Freiburg.


Two-year-olds should learn to code, says computing pioneer

The Guardian

Dame Stephanie Shirley, whose company was one of the first to sell software in the 1960s, said that engaging very young children – in particular girls – could ignite a passion for puzzles and problem-solving long before the "male geek" stereotype took hold. "I don't think you can start too early," she said, adding that evidence suggested that the best time to introduce children to simple coding activities was between the ages of two and seven years. "Companies run by women still have extraordinary difficulty in getting venture capital," she said. Such technology is already being tested at Priors Court in Berkshire, a residential school for autistic children that Shirley founded.


Machine-learning algorithms can dramatically improve ability to predict suicide attempts

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After a meta-analysis, or a synthesis of the results in these published studies, they found that no single risk factor had clinical significance in predicting suicidal ideation, attempts or completion. The authors also found that the ability of researchers to find factors that predict suicidal thoughts and behaviors did not improve over the 50 years they surveyed, and that some of the most popular factors to study--including mood disorders, substance abuse and demographics--are some of the weakest predictors. "Few would expect hopelessness measured as an isolated trait-like factor to accurately predict suicide death over the course of a decade," the researchers write. Colin Walsh, an internist and data scientist at Vanderbilt University Medical Center, along with FSU's Franklin and Ribeiro, looked at millions of anonymized health records and compared 3,250 clear cases of nonfatal suicide attempts with a random group of patients.


A.I. Using Neuroscience? - The Ape Machine

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A good rule of thumb a the moment is to mentally replace the words "artificial intelligence" with "machine learning" at the moment, and educate yourself on the difference. Once you have this little adjustment taken care of, it will be much easier to distinguish between machine learning models that are performing a task, often more efficiently than any human could ever do, bounded by the parameters of this one task, and more generalistic (true) artificial intelligence, which should perform more like a human would, or at least that is what many people hope to achieve. I used to be a real believer in spiking neural networks, even though their practical application is minimal at the moment, I do think they will mature and become highly efficient in performing tasks, maybe limited in scope, maybe more generalistic. On the one hand we have people looking into building "neural laces" and the likes, to make sure we can augment human intelligence to keep up with the machines of the future, posing that human intelligence is limited in bandwidth, yet we want to model this inferior intelligence in machines for some reason.


Smart computers decode brain activity

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The aim of the research was to build upon studies that are showing how computer science and artificial intelligence can take brain research in new directions. The study demonstrates how a self-learning algorithm can decodes human brain signals, as measured by an electroencephalogram. Such research is directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. The aim of this was to further understand the diverse intersections between human and machine and to better develop artificial intelligence for medical science, in relation to interpreting brains scans.


Is It Possible To Wipe Out Those Bad Memories?

International Business Times

So, weakening these connections can help in erasing the memories, the research published Thursday in the journal, Neuron, reveals. The findings of the research -- conducted using the mouse as a model -- offers insight into the treatment of Post Traumatic Stress Disorder (PTSD) and specific phobias. In a study published in 2014 in journal Social Cognitive and Affective Neuroscience, the researchers suggested a simple strategy to reduce the negative effects of these memories. "Looking away from the worst emotions and thinking about the context, like a friend who was there or what the weather was like, will rather effortlessly take your mind away from the unwanted emotions associated with a negative memory," psychology professor Florin Dolcos of the Cognitive Neuroscience Group, who led the research at the Beckman Institute at the University of Illinois, said.


How we recall the past

MIT News

When we have a new experience, the memory of that event is stored in a neural circuit that connects several parts of the hippocampus and other brain structures. Previous research has shown that encoding these memories involves cells in a part of the hippocampus called CA1, which then relays information to another brain structure called the entorhinal cortex. In one group of mice, the MIT team inhibited neurons of the subiculum as the mice underwent fear conditioning, which had no effect on their ability to later recall the experience. However, in another group, they inhibited subiculum neurons after fear conditioning had occurred, when the mice were placed back in the original chamber.


Neuroscience and Machine Learning Restore Movement in Paralyzed Man's Hand

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The engineers at Battelle worked with physicians and neuroscientists from Ohio State University Wexner Medical Center to develop the research approach and perform the clinical study. In 2014, Ohio State surgeons implanted a chip in Ian's brain. The team used brain imaging to identify and isolate the part of Mr. Burkhart's brain that controls hand movements. Through repetition, the firing patterns were analyzed and used to develop an algorithm to control the muscles in his hand.