brain


Artificial neural networks now able to help reveal a brain's structure

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The function of the brain is based on the connections between nerve cells. In order to map these connections and to create the connectome, the "wiring diagram" of a brain, neurobiologists capture images of the brain with the help of three-dimensional electron microscopy. Up until now, however, the mapping of larger areas has been hampered by the fact that, even with considerable support from computers, the analysis of these images by humans would take decades. Scientists from Google AI and the Max Planck Institute of Neurobiology describe a method based on artificial neural networks that is able to reconstruct entire nerve cells with all their elements and connections almost error-free from image stacks. This milestone in the field of automatic data analysis could bring us much closer to mapping and in the long term also understanding brains in their entirety.


The big problem with big data? Without theory, it's just garbage

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Uta Frith doesn't want to meet Donald Trump. "There would be no point in my saying anything to him," she says. "Mostly, when scientists give advice to politicians, politicians listen only to the things they want to hear." Frith, a developmental psychologist who works at University College London, should know. Not only has she been a pioneer in the study of dyslexia and autism -- in the 1960s, she was one of the first researchers in the UK to study Asperger's Syndrome -- but she has also been working to advance the interests of women in science for decades.


Workers need not worry: Two skeptical views of artificial intelligence

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I have been compiling evidence and argumentation that artificial intelligence (AI) will not (any time soon or probably ever) match or exceed our most important human abilities. Many current AI projects have much to offer -- in medical research, autonomous vehicles, and across science and the economy. Deep learning and other AI techniques can process and parse previously unimaginable volumes of data, make sense of complex systems, and even mimic some human senses, such as vision and hearing. As for the fashionable economic worry that AI is a widespread threat to employment, however, I'm skeptical. Among many new entries in the growing literature of AI reality, let's highlight two.


Your first memory probably isn't yours, no matter how real it seems

Popular Science

Think back to your earliest memory. What age were you in it? In a recent survey, 40 percent of people say they remember events earlier than age two. But here's the problem: Most memory researchers argue that its essentially impossible to remember anything before those terrible twos. Understanding how and why our brains form memories in the first place might convince you that if you're in that 40 percent, perhaps your memory is a fictional one after all.


Google researchers create AI that maps the brain's neurons

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Mapping the structure of biological networks in the nervous system -- a field of study known as connectomics -- is computationally intensive. The human brain contains around 86 billion neurons networked through 100 trillion synapses, and imaging a single cubic millimeter of tissue can generate more than 1,000 terabytes of data. Luckily, artificial intelligence can help. In a paper (High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks) published in the journal Nature Methods, scientists at Google and the Max Planck Institute of Neurobiology demonstrated a recurrent neural network -- a type of machine learning algorithm that's often used in handwriting and speech recognition -- tailored made for connectomics analysis. Google researchers aren't the first to apply machine learning to connectomics -- in March, Intel partnered with the Massachusetts Institute of Technology's Computer Science and AI Laboratory to develop a "next-gen" brain image processing pipeline.


AI can untangle the jumble of neurons packed in brain scans

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Video AI can help neurologists automatically map the connections between different neurons in brain scans, a tedious task that can take hundreds and thousands of hours. In a paper published in Nature Methods, AI researchers from Google collaborated with scientists from the Max Planck Institute of Neurobiology to inspect the brain of a Zebra Finch, a small Australian bird renowned for its singing. Although the contents of their craniums are small, Zebra Finches aren't birdbrains, their connectome* is densely packed with neurons. To study the connections, scientists study a slice of the brain using an electron microscope. It requires high resolution to make out all the different neurites, the nerve cells extending from neurons.


AI can untangle the jumble of neurons packed in brain scans

#artificialintelligence

Video AI can help neurologists automatically map the connections between different neurons in brain scans, a tedious task that can take hundreds and thousands of hours. In a paper published in Nature Methods, AI researchers from Google collaborated with scientists from the Max Planck Institute of Neurobiology to inspect the brain of a Zebra Finch, a small Australian bird renowned for its singing. Although the contents of their craniums are small, Zebra Finches aren't birdbrains, their connectome* is densely packed with neurons. To study the connections, scientists study a slice of the brain using an electron microscope. It requires high resolution to make out all the different neurites, the nerve cells extending from neurons.


Always Learning, Always Growing: How Neural Networks Do The Hard Work

Forbes Technology

Not that he was overly excited about it: Rosenblatt told The New Yorker that he thought the machine was "of no practical use." Sixty years later we can safely say Rosenblatt underestimated his invention. True to its name, the Mark 1 in fact marked the first artificial neural network. The way it worked, simply, was this: The Mark 1 Perceptron used 400 randomly connected photocells to "see" a triangle--that is, not just capture its image the way a camera might, but in fact to "recognize" it for future reference. Today, neural networks and neurocomputing have revolutionized artificial intelligence (AI) and made great advances in deep learning possible.


Will AI Be Able to Predict Your Dreams? – ReadWrite

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Have you ever had an unsettling dream about some random person you went to high school with? Or a dream that predicted the future in some idiosyncratic way, like foreseeing a traffic jam on your way to work? Or how about some nonsense dream with random creatures and characters, with no discernable plot, unfolding as you sleep? Dreams are part of the human experience, and one that's baffled mystics, scientists, and everyday people alike. On the surface, dreams can be profound and confusing, and underneath, their scientific basis continues to elude dream psychologists and biologists alike.


Machine learning analyzes MRIs to identify schizophrenia with 78% accuracy

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Researchers have shown that machine learning can identify if a patient has schizophrenia by analyzing an MRI of their brain, according to a new study published in Molecular Psychiatry. After the algorithm studied MRIs of patients with and without schizophrenia, the authors found it could successfully identify which patients had the disorder at a rate of 78 percent. The algorithm also predicted which patients would respond positively to being treated with risperidone at a rate of 82 percent. The key was examining connections in the brain's superior temporal cortex to other parts of the brain. "This is the first step, but ultimately we hope to find reliable biomarkers that can predict schizophrenia before the symptoms show up," author Bo Cao, an assistant professor of psychiatry at the University of Alabama, said in a news release.