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Henry Gustave Molaison was a man who couldn't make memories. Better known to neuroscientists as "HM", the late Molaison suffered from seizures as a young man and struggled to lead a normal life, but things took a dramatic shift after he received a lobotomy in August 1953. Doctors removed large chunks of HM's temporal lobes and most of his hippocampus, on the assumption that these regions were responsible for the patient's neurological problems. The operation did cure HM's seizures, but it left him in a unique case of anterograde amnesia; he could remember his childhood and his personality remained unchanged, but he could not form new memories.

As Steven Shapin writes in a piece for the New Yorker this week, the operation left HM in a constant state of discovery and confusion, but it also gave scientists remarkable new insight into how the brain processes and stores memory.

"The operation could not have been better designed if the intent had been to create a new kind of experimental object that showed where in the brain memory lived," Shapin writes. "Molaison gave scientists a way to map cognitive functions onto brain structures. It became possible to subdivide memory into different types and to locate their cerebral Zip Codes."

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Original author: 
Ben Popper

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The field of neuroscience has been animated recently by the use of Functional Magnetic Resonance Imaging, or fMRI. When a person lies in an fMRI machine, scientists can see their brain activity in real time. It’s a species of mind reading that promises to unlock the still mysterious workings of our grey matter.

In April, a team in Japan announced that they could identify when a subject was dreaming about different types of objects like a house, a clock, or a husband. Last November, another group of researchers using this technique was able to predict if gadget columnist David Pogue was thinking about a skyscraper or a strawberry.

What earlier studies couldn’t determine, however, was how the subjects were actually feeling. A new...

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Original author: 
Soulskill

vinces99 writes "Small electrodes placed on or inside the brain allow patients to interact with computers or control robotic limbs simply by thinking about how to execute those actions. This technology could improve communication and daily life for a person who is paralyzed or has lost the ability to speak from a stroke or neurodegenerative disease. Now researchers have demonstrated that when humans use this brain-computer interface, the brain behaves much like it does when completing simple motor skills such as kicking a ball, typing or waving a hand (abstract). That means learning to control a robotic arm or a prosthetic limb could become second nature for people who are paralyzed."

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Original author: 
Soulskill

An anonymous reader writes "We're seeing a new revolution in artificial intelligence known as deep learning: algorithms modeled after the brain have made amazing strides and have been consistently winning both industrial and academic data competitions with minimal effort. 'Basically, it involves building neural networks — networks that mimic the behavior of the human brain. Much like the brain, these multi-layered computer networks can gather information and react to it. They can build up an understanding of what objects look or sound like. In an effort to recreate human vision, for example, you might build a basic layer of artificial neurons that can detect simple things like the edges of a particular shape. The next layer could then piece together these edges to identify the larger shape, and then the shapes could be strung together to understand an object. The key here is that the software does all this on its own — a big advantage over older AI models, which required engineers to massage the visual or auditory data so that it could be digested by the machine-learning algorithm.' Are we ready to blur the line between hardware and wetware?"

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Original author: 
Adi Robertson

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Stanford Professor Andrew Ng is bringing back the idea of an artificial intelligence that can think like a person. With Google's Deep Learning project, he's creating machines that take a multi-layered approach to information, building up knowledge and figuring out concepts by passing data between various networks that can each recognize a small piece of it. The approach is designed to mimic how the human brain processes information with neural networks, and it's starting to work — last year, Google's "brain" figured out how to identify cats in YouTube videos without being told that the concept of "cat" existed. Wired has profiled Ng and his work on brain-like computers, a project that also ties into current government-funded brain...

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Original author: 
John Timmer

Arthur Toga, University of California, Los Angeles

In 1992, at the age of 70, a US citizen suffered a severe case of viral encephalitis, a swelling of the brain caused by infection. After he recovered two years later, he appeared completely average based on an IQ test (indeed, he scored 103). Yet in other ways, he was completely different. Several decades of his past life were wiped completely from his brain. His only accessible memories came from his 30s, and from the point of his illness to his death, he would never form another memory that he was aware of.

But this severe case of what appears to be total amnesia doesn't mean he had no memory as we commonly understand it. The patient, called E.P., was studied intensely using a battery of tests for more than a decade, with researchers giving him tests during hundreds of sessions. After his death, his brain was given for further study. With the analysis of the brain complete, the people who studied him have taken the opportunity to publish a review of all his complex memory problems.

Aside from memory, there were only a few obvious problems with E.P. Most of his senses were normal except smell, which was wiped out (a condition called anosmia). His vision was perfectly fine, but he had two specific problems interpreting what he saw. One was a limited ability to discriminate between faces, and the other was difficulty in determining whether a line drawing represented an object that's physically impossible (think M. C. Escher drawings).

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"Thanks for the memories, but I'd prefer a bite to eat."

UFL.edu

As the organ responsible for maintaining equilibrium in the body and the most energy-demanding of all the organs, the brain takes a lot of the body's energy allocation. So when food is in short supply, the brain is the organ that is fed first. But what happens when there isn’t enough food to fulfill the high-energy needs of the brain and survival is threatened?

The brain does not simply self-allocate available resources on the fly; instead it “trims the fat” by turning off entire processes that are too costly. Researchers from CNRS in Paris created a true case of do-or-die, starving flies to the point where they must choose between switching off costly memory formation or dying. When flies are starved, their brains will block the formation of aversive long-term memories, which depend on costly protein synthesis and require repetitive learning.

But that doesn't mean all long-term memories are shut down. Appetitive long-term memories, which can be formed after a single training, are enhanced during a food shortage.

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Pyramidal neurons have a distinctive shape and set of connections.

Gao lab, Drexel

The cerebral cortex—the gray matter that forms the outer layers of the mammalian cerebrum and cerebellum—is divided into six different layers based on the presence of specialized neurons, and we've known that since the early 1900s. Denis Jabaudon is interested in using the tools of modern biology to understand the genetic mechanisms that establish and maintain those layers. Over the past few years, his lab has published papers implicating various genes in the generation of specific neuronal subtypes.

Now they have gone a step further. They have developed a new electrochemical method to transfer genes into specific types of neurons—they call it iontoporation. Using it, they have transformed one type of neuron in a mature brain into a different type entirely. (Imagine a lightning bolt and crash of thunder here to indicate how momentous and scary this is.) Just kidding—it’s not actually scary. Instead, it tells us something about the ability of a mature brain to adapt to being rewired.

Although Jabaudon and others have made some headway in working out how the different neurons arise, they still don’t know how plastic they are—if they can change fates after they started differentiating down one particular path. In the context of brain injury, it would be useful to know if certain neural circuits could be reprogrammed and repaired by having the neurons that are already present change fates to adapt to the damage. But this has been challenging to determine, because changing the fate of specific neurons in the latter stages of differentiation has been technically difficult.

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Mind-controlled Machines: Jose del R. Millan at TEDxZurich

The idea of controlling machines not by manual operation, but by mere "thinking" (ie, the brain activity of human subjects) has always fascinated humankind. A brain-machine interface (BMI) makes this truly possible as it monitors the user's brain activity and translates their intentions into actions, such as moving a wheelchair or selecting a letter from a virtual keyboard. The central tenet of a BMI is the capability to distinguish different patterns of brain activity each being associated to a particular intention or mental task. This is a real challenge which is far from being solved! BMI holds a high, perhaps bold, promise: human augmentation through the acquisition of new brain capabilities that will allow us to communicate and interact with our environment directly by "thinking". This is particularly relevant for physically-disabled people but is not limited to them. Yet, how is it possible to fulfill this dream using a "noisy channel" like brain signals? Which are the principles that allow people operate complex brain-controlled robots over long periods of time? Jose del R. Millan is the Defitech Professor at the Ecole Polytechnique Federale de Lausanne (EPFL) where he explores the use of brain signals for multimodal interaction and, in particular, the development of non-invasive brain-controlled robots and neuroprostheses. In this multidisciplinary research effort, Dr. Millán is bringing together his pioneering work on the two fields of brain-machine interfaces <b>...</b>
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