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Your Brain’s Memory Capacity May Be as Big as the World Wide Web

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In an attempt to understand and measure the brain’s synapses, whose shape and size have remained mysterious to scientists, researchers at the University of Texas, Austin and the Salk Institute worked together to determine that the brain’s memory capacity is much larger than previously understood. The results, published in the journal eLife, estimate that an individual human brain may store as much as a petabyte of information—perhaps 10 times more than previously estimated, and about the equivalent of the World Wide Web.

The study was the first attempt “to reconstruct in three dimensions every single synapse and associated structure in a brain region,” to try to understand “basic synaptic structure and local connectivity among neurons,” Kristen Harris, co-senior author of the study and professor of neuroscience at UT Austin, tells mental_floss.

Synapses communicate signals between neurons. They're formed when the cable-like axon from one neuron connects with a "spine" on a dendrite, a branch-like structure extending from the neural cell body, of another. To better understand the way synaptic storage is measured, consider that a computer’s memory is measured in bits, each of which can have a value of 0 or 1. "In the brain, information is stored in the form of synaptic strength, a measure of how strongly activity in one neuron influences another neuron to which it is connected,” write the authors. “The number of different strengths can be measured in bits. The total storage capacity of the brain therefore depends on both the number of synapses and the number of distinguishable synaptic strengths."

Researchers were able to see these synapses by analyzing thin slices of tissue from the hippocampus—the brain region connected to learning and memory—from three male adult rats using electron microscopy. Then, over several years, they used computer software to reconstruct in 3D every “structural process” and roughly 500 synapses found in a tiny section of brain tissue the size of a single red blood cell.

They identified places where two neurons were connected to each other through two synapses, called "axon-coupled pairs,” which allowed them to estimate new sizes of synapses. What they found were 26 different “bins” of synapses that can store 4.7 bits of information each.

Not only is the diversity of synapses they observed in such a small brain region surprising, the storage capacity of each is “markedly higher than previous suggestions,” write the authors. Prior to this, researchers believed an individual synapse was only capable of storing 1 to 2 bits of information. This suggests we may have underestimated the memory capacity of the brain, which has trillions of synapses, "by an order of magnitude."

According to lead author Terry Sejnowski, in whose lab the study was conducted, "Our new measurements of the brain’s memory capacity increase conservative estimates by a factor of 10 to at least a petabyte, in the same ballpark as the World Wide Web." 

The research provides researchers who study memory and learning with a deeper understanding of the brain’s memory capacity, and a new dataset to work with. “This is just the beginning—a tiny chink in the mysterious armor of the structure and function of synapses in the brain,” Harris says.

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iStock // Ekaterina Minaeva
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Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]

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