All posts filed under: Artificial Intelligence, Science, Technology

Articles relating to cognitive science, neurobiology, Artificial Intelligence and related fields

Three dramatic new ways to visualize brain tissue and neuron circuits

May lead to breakthroughs in tracking brain disorders such as autism, schizophrenia, and Alzheimer’s May 7, 2018 (NOT written by David Wolf; collected from Kurzweil AI and reproduced here.) Visualizing the brain: Here, tissue from a human dentate gyrus (a part of the brain’s hippocampus that is involved in the formation of new memories) was imaged transparently in 3D and colored-coded to reveal the distribution and types of nerve cells. (credit: The University of Hong Kong) Visualizing human brain tissue in vibrant transparent colors Neuroscientists from The University of Hong Kong (HKU) and Imperial College London have developed a new method called “OPTIClear” for 3D transparent color visualization (at the microscopic level) of complex human brain circuits. To understand how the brain works, neuroscientists map how neurons (nerve cells) are wired to form circuits in both healthy and disease states. To do that, the scientists typically cut brain tissues into thin slices. Then they trace the entangled fibers across those slices — a complex, laborious process. Making human tissues transparent. OPTIClear replaces that process by “clearing” …

The brain learns differently than we’ve assumed, new learning theory says

March 28, 2018 A revolutionary new theory contradicts a fundamental assumption in neuroscience about how the brain learns. According to researchers at Bar-Ilan University in Israel led by Prof. Ido Kanter, the theory promises to transform our understanding of brain dysfunction and may lead to advanced, faster, deep-learning algorithms. New post-Hebb brain-learning model may lead to new brain treatments and breakthroughs in faster deep learning. A biological schema of an output neuron, comprising a neuron’s soma (body, shown as gray circle, top) with two roots of dendritic trees (light-blue arrows), splitting into many dendritic branches (light-blue lines). The signals arriving from the connecting input neurons (gray circles, bottom) travel via their axons (red lines) and their many branches until terminating with the synapses (green stars). There, the signals connect with dendrites (some synapse branches travel to other neurons), which then connect to the soma. (credit: Shira Sardi et al./Sci. Rep) The brain is a highly complex network containing billions of neurons. Each of these neurons communicates simultaneously with thousands of others via their synapses. A neuron collects its many synaptic incoming signals …

Physicists Negate Century-Old Assumption Regarding Neurons and Brain Activity

 Using new types of experiments on neuronal cultures, a group of scientists, led by Prof. Ido Kanter, of the Department of Physics at Bar-Ilan University, has demonstrated that this century-old assumption regarding brain activity is mistaken. (NeuroscienceNews.com image is in the public domain.) The new realization for the computational scheme of a neuron calls into question the spike sorting technique which is at the center of activity of hundreds of laboratories and thousands of scientific studies in neuroscience. This method was mainly invented to overcome the technological barrier to measure the activity from many neurons simultaneously, using the assumption that each neuron tends to fire spikes of a particular waveform which serves as its own electrical signature. However, this assumption, which resulted from enormous scientific efforts and resources, is now questioned by the work of Kanter’s lab. (See abstract below.) ABOUT THIS NEUROSCIENCE RESEARCH ARTICLE Funding: This research is supported in part by the TELEM grant of the Council for Higher Education in Israel. Source: Elana Oberlander – Bar-Ilan University Publisher: Organized by NeuroscienceNews.com. Image Source: NeuroscienceNews.com image is credited to …

The Dark Secret at the Heart of AI

No one really knows how the most advanced algorithms do what they do. That could be a problem. by Will Knight April 11, 2017 Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result …

Why our brains may be 100 times more powerful than believed

The dendrites in our brain have been underestimated for 60 years says a new study (Credit:vitstudio/Depositphotos) A new study out of the University of California Los Angeles (UCLA) has found that one part of the neurons in our brains is more active than previously revealed. The finding implies that our brains are both analog and digital computers and could lead to better ways to treat neurological disorders. The focus of the study was the dendrites, long branch-like structures that attach to a roundish body called the soma to form neurons. It was previously believed that dendrites were nothing more than conduits that sent spikes of electrical activity generated in the soma to other neurons. But the study has shown that the dendrites themselves are highly active, sending spikes of their own at a rate 10 times that previously believed. The finding runs counter to the long-held belief that somatic spikes were the main way we learn and form memories and perceptions. “Dendrites make up more than 90 percent of neural tissue,” said UCLA neurophysicist Mayank …

Electronic synapses that can learn: First step towards an artificial brain?

Artist’s impression of the electronic synapse: the particles represent electrons circulating through oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses. Credit: © Sören Boyn / CNRS/Thales physics joint research unit. Researchers from the CNRS, Thales, and the Universities of Bordeaux, Paris-Sud, and Evry have created an artificial synapse capable of learning autonomously. They were also able to model the device, which is essential for developing more complex circuits. The research was published in Nature Communications on 3 April 2017. One of the goals of biomimetics is to take inspiration from the functioning of the brain in order to design increasingly intelligent machines. This principle is already at work in information technology, in the form of the algorithms used for completing certain tasks, such as image recognition; this, for instance, is what Facebook uses to identify photos. However, the procedure consumes a lot of energy. Vincent Garcia (Unité mixte de physique CNRS/Thales) and his colleagues have just taken a …

The future of AI is neuromorphic. Meet the scientists building digital ‘brains’ for your phone

Neuromorphic chips are being designed to specifically mimic the human brain – and they could soon replace CPUs AI services like Apple’s Siri and others operate by sending your queries to faraway data centers, which send back responses. The reason they rely on cloud-based computing is that today’s electronics don’t come with enough computing power to run the processing-heavy algorithms needed for machine learning. The typical CPUs most smartphones use could never handle a system like Siri on the device. But Dr. Chris Eliasmith, a theoretical neuroscientist and co-CEO of Canadian AI startup Applied Brain Research, is confident that a new type of chip is about to change that. “Many have suggested Moore’s law is ending and that means we won’t get ‘more compute’ cheaper using the same methods,” Eliasmith says. He’s betting on the proliferation of ‘neuromorphics’ — a type of computer chip that is not yet widely known but already being developed by several major chip makers. Traditional CPUs process instructions based on “clocked time” – information is transmitted at regular intervals, as …

Artificial Synapse Developed for Neural Networks

For all the improvements in computer technology over the years, we still struggle to recreate the low-energy, elegant processing of the human brain. Now, researchers at Stanford University and Sandia National Laboratories have made an advance that could help computers mimic one piece of the brain’s efficient design — an artificial version of the space over which neurons communicate, called a synapse. “It works like a real synapse but it’s an organic electronic device that can be engineered,” said Alberto Salleo, associate professor of materials science and engineering at Stanford and senior author of the paper. “It’s an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that’s been done before with inorganics.” The new artificial synapse, reported in the Feb. 20 issue of Nature Materials, mimics the way synapses in the brain learn through the signals that cross them. This is a significant energy savings over traditional computing, which involves separately processing information and then storing it into …

Researchers uncover algorithm which may solve human intelligence

By Charlie Osborne for Between the Lines | November 29, 2016 If we have the algorithm, we also have the key to true artificial intelligence. The key element which separates today’s artificial intelligence (AI) systems and what we consider to be human thought and learning processes could be boiled down to no more than an algorithm. That’s according to a recent paper published in the journal Frontiers in Systems Neuroscience, which suggests that despite the complexity of the human brain, an algorithm may be all it takes for our technological creations to mimic our way of thinking. As reported by Business Insider, the idea that human thought can be whittled down to an algorithm lies in the “Theory of Connectivity,” which proposes that human intelligence is rooted in “a power-of-two-based permutation logic (N = 2i-1)” algorithm, capable of producing perceptions, memories, generalized knowledge and flexible actions, according to the paper. First proposed in 2015, the theory suggests that how we acquire and process knowledge can be explained by how different neurons interact and align in …

Can Quantum Physics Explain Consciousness?

Written by JENNIFER OUELLETTE  NOV 7, 2016  The Atlantic A new approach to a once-farfetched theory is making it plausible that the brain functions like a quantum computer. The mere mention of “quantum consciousness” makes most physicists cringe, as the phrase seems to evoke the vague, insipid musings of a New Age guru. But if a new hypothesis proves to be correct, quantum effects might indeed play some role in human cognition. Matthew Fisher, a physicist at the University of California, Santa Barbara, raised eyebrows late last year when he published a paper in Annals of Physics proposing that the nuclear spins of phosphorus atoms could serve as rudimentary “qubits” in the brain—which would essentially enable the brain to function like a quantum computer. As recently as 10 years ago, Fisher’s hypothesis would have been dismissed by many as nonsense. Physicists have been burned by this sort of thing before, most notably in 1989, when Roger Penrose proposed that mysterious protein structures called “microtubules” played a role in human consciousness by exploiting quantum effects. Few researchers believe …

The Map of the Human Brain Is Finally Getting More Useful

                  The new human brain map has 180 regions on both the left and right halves. By Ryan Cross July 20, 2016 Human Connectome Project neuroscientists have created a program to make individualized brain maps.  The human brain is a little bit less of a mystery today, thanks to new maps from neuroscientists at Washington University Medical School. Not only did they identify more brain regions than previous maps, they also made a machine-learning program to re-create a new map for any brain, which will help scientists and doctors study individual differences in brain structure and disease, and will hopefully lead to new ways to diagnose brain disorders. The new map of the brain’s outermost crinkled layer, called the cerebral cortex, was published in Nature today. David Van Essen, the lead mapmaker, calls it a landmark study for the Human Connectome Project, which he heads. Researcher Matthew Glasser says that unlike many previous studies, this map considers several features of the brain simultaneously to mark its boundaries. Some neuroscientists …

The mind isn’t locked in the brain but extends far beyond it

The author of this piece is Keith Frankish , an English philosopher and writer. He is a visiting research fellow with the Open University in the UK and an adjunct professor with the Brain and Mind Programme at the University of Crete. He lives in Greece. Where is your mind? Where does your thinking occur? Where are your beliefs? René Descartes thought that the mind was an immaterial soul, housed in the pineal gland near the centre of the brain. Nowadays, by contrast, we tend to identify the mind with the brain. We know that mental processes depend on brain processes, and that different brain regions are responsible for different functions. However, we still agree with Descartes on one thing: we still think of the mind as (in a phrase coined by the philosopher of mind Andy Clark)brainbound, locked away in the head, communicating with the body and wider world but separate from them. And this might be quite wrong. I’m not suggesting that the mind is non-physical or doubting that the brain is central to it; but it …

Mind transfer is the future of the post-mortal human

Mortality has long been a thorn in the eye of human, tormenting the mind for centuries. Cold, gripping realization that death is inevitable and the speculation of afterlife has always been an object of obsession for many. Through its many forms, death is a literal end of the road, end of a journey long started. What if that road could continue, if that journey was ongoing? If there was a way to achieve proverbial life after death, transcending universe and material existence? [NOTE: This is precisely the issue that forms the basis for my 5-star science fiction novel, Mindclone. See at this link: https://www.amazon.com/Mindclone-youre-brain-without-called-ebook/dp/B00BJWOHDE ] Brain uploading With the rapid technological advancement, the desire to upload the contents of human brains into a machine grows with each passing day. There is a strong belief that this would allow anyone to live forever. Inside a machine entity, they’d form super AI. Every once in awhile, some neuroscientist reveals his or her project on brain mapping and other various similar themes. The trend is catching on and it slowly …

Smart Dust Is Coming: New Camera Is the Size of a Grain of Salt

BY JASON DORRIER ON JUN 28, 2016 The array of doublet lenses pictured here were printed directly onto a CMOS image sensor. Image credit: Timo Gissibl/University of Stuttgart Miniaturization is one of the most world-shaking trends of the last several decades. Computer chips now have features measured in billionths of a meter. Sensors that once weighed kilograms fit inside your smartphone. But it doesn’t end there. Researchers are aiming to take sensors smaller—much smaller. In a new University of Stuttgart paper published in Nature Photonics, scientists describe tiny 3D printed lenses and show how they can take super sharp images. Each lens is 120 millionths of a meter in diameter—roughly the size of a grain of table salt—and because they’re 3D printed in one piece, complexity is no barrier. Any lens configuration that can be designed on a computer can be printed and used. This allows for a variety of designs to be tested to achieve the finest quality images. According to the paper, the new method not only demonstrates high-quality micro-lenses can be 3D printed, but it also solves roadblocks to current …