Researchers Discover How the Brain Paralyzes You While You Sleep

TOPICS:NeurosciencePhysiologySleep ScienceUniversity Of Tsukuba

By UNIVERSITY OF TSUKUBA JANUARY 17, 2021

Researchers at the University of Tsukuba in Japan have discovered a group of neurons in the mouse brainstem that suppress unwanted movement during rapid eye movement sleep.

We laugh when we see Homer Simpson falling asleep while driving, while in church, and while even operating the nuclear reactor. In reality though, narcolepsy, cataplexy, and rapid eye movement (REM) sleep behavior disorder are all serious sleep-related illnesses. Researchers at the University of Tsukuba led by Professor Takeshi Sakurai have found neurons in the brain that link all three disorders and could provide a target for treatments.

REM sleep correlates when we dream. Our eyes move back and forth, but our bodies remain still. This near-paralysis of muscles while dreaming is called REM-atonia, and is lacking in people with REM sleep behavior disorder. Instead of being still during REM sleep, muscles move around, often going as far as to stand up and jump, yell, or punch. Sakurai and his team set out to find the neurons in the brain that normally prevent this type of behavior during REM sleep.

Working with mice, the team identified a specific group of neurons as likely candidates. These cells were located in an area of the brain called the ventral medial medulla and received input from another area called the sublaterodorsal tegmental nucleus, or SLD. “The anatomy of the neurons we found matched what we know,” explains Sakurai. “They were connected to neurons that control voluntary movements, but not those that control muscles in the eyes or internal organs. Importantly, they were inhibitory, meaning that they can prevent muscle movement when active.” When the researchers blocked the input to these neurons, the mice began moving during their sleep, just like someone with REM sleep behavior disorder.

Narcolepsy, as demonstrated by Homer Simpson, is characterized by suddenly falling asleep at any time during the day, even in mid-sentence (he was diagnosed with narcolepsy). Cataplexy is a related illness in which people suddenly lose muscle tone and collapse. Although they are awake, their muscles act as if they are in REM sleep. Sakurai and his team suspected that the special neurons they found were related to these two disorders. They tested their hypothesis using a mouse model of narcolepsy in which cataplexic attacks could be triggered by chocolate. “We found that silencing the SLD-to-ventral medial medulla reduced the number of cataplexic bouts,” says Sakurai.

Overall, the experiments showed these special circuits control muscle atonia in both REM sleep and cataplexy. “The glycinergic neurons we have identified in the ventral medial medulla could be a good target for drug therapies for people with narcolepsy, cataplexy, or REM sleep behavior disorder”, says Sakurai. “Future studies will have to examine how emotions, which are known to trigger cataplexy, can affect these neurons.”

Reference: “A discrete glycinergic neuronal population in the ventromedial medulla that induces muscle atonia during REM sleep and cataplexy in mice” by Shuntaro Uchida, Shingo Soya, Yuki C. Saito, Arisa Hirano, Keisuke Koga, Makoto Tsuda, Manabu Abe, Kenji Sakimura and Takeshi Sakurai, 28 December 2020, Journal of Neuroscience.
DOI: 10.1523/JNEUROSCI.0688-20.2020

We recommend

1. “Just Go To Sleep!” Sleep & LearningJenny L. Williamson et al., The American Biology Teacher, 2014
2. A systems approach to self‐organization in the dreaming brainStanley Krippner et al., Kybernetes, 2002
3. A case of attempted bilateral self-enucleation in a patient with bipolar disorderHannah Muniz Castro et al., Mental Illness, 2017

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2. Electromyography features during physical and imagined standing up in healthy young adultsKanokwan Srisupornkornkool et al., Journal of Health Research, 2020

Chromecast w/ Google TV ‘data may be corrupt’ issue affects some users, here’s how to fix it

Ben Schoon

– Jan. 19th 2021 2:00 pm PT

@NexusBen

One of our favorite streaming devices available today is the $49 Chromecast with Google TV. However, since the device launched in September, quite a few users have run into a problem with the Chromecast with Google TV crashing and showing a “can’t load Android system, your data may be corrupt” screen. Here’s how to fix that if it happens to you.

Unlike previous Chromecast releases, the new Google TV version runs on top of an Android-based operating system. That comes with a ton of advantages, but it also leaves the device subject to some errors that can happen with Android. In this case, that’s with an uncommon issue where the operating system crashes and is unable to reboot properly. This doesn’t happen often, but it seems to be an unfortunately common problem on the Chromecast with Google TV.

Looking around Twitter, Reddit, and Google’s own support forums, there are plenty of examples of users who are being affected by this problem. Reports can easily be found as far back as November 2020 as well as this very month, January 2021. It doesn’t seem like updated software builds or the date of the hardware production has any role in causing the problem.

In any case, though, this error results in the system putting up a blue and black error screen that says that the Chromecast with Google TV “can’t load Android system” and that “your data may be corrupt.” It’s a daunting screen for anyone who hasn’t encountered it before but, thankfully, there are some easy workarounds here.

How to fix ‘data may be corrupt’ Chromecast with Google TV error

The common mistake when trying to fix this problem is trying to use the remote included with Chromecast. When any Android device goes into this state, it effectively cuts itself off from everything else. On a phone, that means wireless accessories and the touchscreen are inoperable. On Chromecast with Google TV, it means the remote doesn’t work, though you wouldn’t get that based on the error message.

Rather, you have to navigate this menu by using the singular button on the back of the Chromecast with Google TV, pictured below. That button is used to both navigate and select options in this menu. A single press moves between the options on-screen while a 3-second long-press selects the option which is highlighted in blue.

If you do run into this error, the first thing we’d recommend attempting is a simple reboot using the “Try again” option. To do that, simply press and hold the button for a second or so and the Chromecast will quickly reboot. In many cases, this solves the problem.

If that doesn’t work, you’ll have to perform a factory reset from this page. To do that, press the button once to get the “Factory data reset” option highlighted in blue. From there, simply long-press to select that option. From there, the Chromecast with Google TV will reset itself, wiping all data. After it boots back up, hopefully this time without the “data may be corrupt” error, you’ll need to set up Chromecast with Google TV again from scratch. Luckily, most data will be saved to your Google account which means you’ll just need to log into and download some apps.

More on Chromecast with Google TV:

• Apple TV app coming to Chromecast with Google TV in early 2021
• Discovery+ is now available on Android, Chromecast w/ Google TV, Android TV
• HBO Max won’t support Dolby Atmos on Chromecast with Google TV

FTC: We use income earning auto affiliate links. More.

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Android TVAndroid TV is a version of the Android platform which has been modified by Google to run on televisions with over 5,000 native applications. The platform is often found on devices from Nvidia, Hisense, and Sony, with operator devices also using Android TV.Google TVCHROMECAST WITH GOOGLE TV

About the Author

Ben Schoon

@NexusBen

Ben is a writer and video producer for 9to5Google.

Find him on Twitter @NexusBen. Send tips to schoon@9to5g.com or encrypted to benschoon@protonmail.com.

What’s New in Chrome 88, Available Now

JOE FEDEWA@tallshmo
JAN 19, 2021, 6:40 AM EST | 3 MIN READ

Google released Chrome 88 to the stable channel on January 19, 2021. The new browser version includes some cool changes, including an improved dark theme for Windows 10 and the beginning of less intrusive permission prompts. Here are the highlights.https://googleads.g.doubleclick.net/pagead/ads?guci=2.2.0.0.2.2.0.0&gdpr=0&client=ca-pub-9173525300015284&output=html&h=165&slotname=2986027408&adk=3461730067&adf=2297078214&pi=t.ma~as.2986027408&w=658&fwrn=4&lmt=1611121737&rafmt=11&psa=1&format=658×165&url=https%3A%2F%2Fwww.howtogeek.com%2F709576%2Fwhats-new-in-chrome-88-available-today%2F&flash=0&wgl=1&uach=WyJNYWMgT1MgWCIsIjEwXzExXzYiLCJ4ODYiLCIiLCI4Ny4wLjQyODAuMTQxIixbXV0.&dt=1611121727960&bpp=106&bdt=11433&idt=6186&shv=r20210113&cbv=r20190131&ptt=9&saldr=aa&abxe=1&cookie=ID%3D686e9255808559db-223594adc5c400d9%3AT%3D1604952731%3AS%3DALNI_Mb4Ijxz9gLKdmPCrfBGynu7i8vHxA&correlator=2779317873680&frm=20&pv=2&ga_vid=200700877.1604952734&ga_sid=1611121737&ga_hid=1378172158&ga_fc=0&rplot=4&u_tz=-480&u_his=1&u_java=0&u_h=1050&u_w=1680&u_ah=980&u_aw=1680&u_cd=24&u_nplug=3&u_nmime=4&adx=324&ady=786&biw=1686&bih=900&scr_x=0&scr_y=0&eid=21068769%2C21069719&oid=3&pvsid=636099504375786&pem=261&ref=https%3A%2F%2Fnews.google.com%2F&rx=0&eae=0&fc=896&brdim=0%2C23%2C0%2C23%2C1680%2C23%2C1686%2C980%2C1686%2C900&vis=1&rsz=o%7Co%7CeE%7C&abl=NS&pfx=0&fu=8320&bc=31&ifi=1&uci=a!1&fsb=1&xpc=3hXgMYPWNE&p=https%3A//www.howtogeek.com&dtd=9852

Better Dark Theme Support on Windows 10

Chrome has supported Windows 10’s system-wide dark theme for a while, but Chrome 88 makes it a little better. Dark Theme now applies to scroll bars on many of Chrome’s internal pages. That includes Settings, Bookmarks, History, New Tab Page, and more. It’s not yet present on websites that support dark themes.

No More FTP in Google Chrome

With Chrome 88, Google Chrome no longer supports FTP URLs—in other words, ftp:// addresses.

FTP support is a legacy feature that has no support for encrypted connections (no FTPS). An attacker could modify files you’re downloading in transit, unlike with encrypted HTTPS or FTPS where this isn’t possible. As Chrome and other browsers are shifting toward an always-encrypted web, dropping old protocols like this makes sense.

Google has been working on removing FTP from Chrome for a while, but it was still available for some people—and a flag could enable it. Google’s usage data showed that very few people used FTP. Now, all FTP support is disabled. If you want to use FTP, you’ll need a separate FTP app.

No More Support for Mac OS X Yosemite

Google is officially dropping support for Mac OS X 10.10 Yosemite in Chrome 88. Mac users will need OS X 10.11 El Capitan or newer to use Chrome 88. This shouldn’t come as a shock, since Apple hasn’t supported Yosemite since 2017.

Legacy Browser Add-on Gone for Good

With Chrome 85, Google removed its Legacy Browser Support add-on as the functionality became baked into Chrome. Chrome 88 takes it a step further and disables all installed instances of the add-on.

LBS was designed for IT admins to call up Microsoft Internet Explorer in Chrome for older apps written for that browser as well as intranet sites. Since it’s now built into Chrome, the add-on is unnecessary.

Less Intrusive Permission Requests

Chrome 88 is experimenting with a smaller and less intrusive way to ask for permissions. Instead of the pop-up that covers the website content, a new “chip” appears to the left of the URL.

The chip first appears with full text such as “Use Your Location?” After a few seconds, it minimizes to simply a small icon. Clicking the chip, which appears as a blue oval, brings up the permission prompt you’re used to seeing.

You can try out the new permission “chips” right now by enabling the flag at chrome://flags/#permission-chip

Testing Light & Dark Themes for Chrome OS

Google is testing more defined light and dark themes for Chromebooks. The theme can be toggled from the Quick Settings menu. Themes affect the Shelf, App Launcher, and Quick Settings panel. Not everything is working 100% right now.

If you’d like to try this out on a Chrome OS system, the flag can be enabled at chrome://flags/#dark-light-mode. After you reboot, the Theme toggle will appear in the Quick Settings.

Tab Search Comes to Desktop

Chrome 87 brought a handy Tab Search feature to Chromebooks, but it wasn’t available on Windows, Mac, or Linux. Chrome 88 brings it to those platforms via a Chrome flag.

When it’s enabled, you get a drop-down arrow in the top tab bar that shows all of your open tabs when selected. You can then use the integrated search bar to find the tab you’re looking for.

To get this feature in Chrome 88, enable the Tab Search flag at chrome://flags/#enable-tab-search.

RELATED: How to Search Open Tabs on Google Chrome

Developer Goodies

Much of what’s new in every Chrome release is under the hood, and Chrome 88 is no exception. Google has outlined many of these changes on its developer site and the Chromium blog:

• Digital Goods API: Web apps published in the Google Play Store can now use Play Store billing just like native apps.
• WebXR: AR Lighting Estimation: For AR and VR content on Android, lighting estimation can help to make models feel more natural and like they “fit” better with the user’s environment.
• Anchor target=_blank implies rel=noopener by Default: To defend against “tab-napping” attacks, anchors that target _blank will behave as though rel  is set to noopener.
• CSS aspect-ratio Property: This allows explicitly specifying an aspect ratio for any element to get similar behavior to a replaced element.
• Origin Isolation: Web apps can choose to increase a page’s security in exchange for giving up access to certain APIs.
• JavaScript Engine: Chrome 88 incorporates version 8.8 of the V8 JavaScript engine.

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As always, Chrome will automatically install the update when it’s available. To immediately check for and install any available updates, click menu > Help > About Google Chrome.

RELATED: How to Update Google ChromeREAD NEXT

• › How to Save Posts on Facebook to Read Them Later
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• › How to Turn Off Automatic Emoji Conversion in Slack

JOE FEDEWA
Joe Fedewa is a Staff Writer at How-To Geek. He has close to a decade of experience covering consumer technology and previously worked as a News Editor at XDA Developers. Joe loves all things technology and is also an avid DIYer at heart. He has written thousands of articles, hundreds of tutorials, and dozens of reviews.

Electrical Brain Stimulation May Alleviate Obsessive-Compulsive Behaviors

Noninvasive electrical zaps, tuned specifically to individual brain-activity patterns, appear to reduce checking, hoarding and other compulsions for up to three months

• By Diana Kwon on January 19, 2021

Obsessive-compulsive disorder (OCD) is marked by repetitive, anxiety-inducing thoughts, urges and compulsions, such as excessive cleaning, counting and checking. These behaviors are also prevalent in the general population: one study in a large sample of U.S. adults found more than a quarter had experienced obsessions or compulsions at some point in their life. Although most of these individuals do not develop full-blown OCD, such symptoms can still interfere with daily life. A new study, published on January 18 in Nature Medicine, hints that these behaviors may be alleviated by stimulating the brain with an electrical current—without the need to insert electrodes under the skull.10 Sec

Robert Reinhart, a neuroscientist at Boston University, and his group drew on two parallel lines of research for this study. First, evidence suggests that obsessive-compulsive behaviors may arise as a result of overlearning habits—leading to their excessive repetition—and abnormalities in brain circuits involved in learning from rewards. Separately, studies point to the importance of high-frequency rhythms in the so-called high-beta/low-gamma range (also referred to as simply beta-gamma) in decision-making and learning from positive feedback.

Drawing on these prior observations, Shrey Grover, a doctoral student in Reinhart’s lab, hypothesized with others in the team that manipulating beta-gamma rhythms in the orbitofrontal cortex (OFC)—a key region in the reward network located in the front of the brain—might disrupt the ability to repetitively pursue rewarding choices. In doing so, the researchers thought, the intervention could reduce obsessive-compulsive behaviors associated with maladaptive habits.

To test this hypothesis, Grover and his colleagues carried out a two-part study. The first segment was aimed at identifying whether the high-frequency brain activity influenced how well people were able to learn from rewards. The team recruited 60 volunteers and first used electroencephalography to pinpoint the unique frequencies of beta-gamma rhythms in the OFC that were active in a given individual whilethat persontook part in a task that involved associating symbols with monetary wins or losses. Previous work had shown that applying stimulation based on the particular patterns of rhythms in a person’s brain may enhance the effectiveness of the procedure.

The participants were then split into three groups, all of whom received a noninvasive form of brain stimulation known as transcranial alternating current stimulation (tACS), which was applied to the OFC for 30 minutes over five consecutive days. Each group had a different type of stimulation: One received personalized currents tuned to an individual’s beta-gamma frequencies. Another was exposed to an “active” placebo, consisting of stimulations at a lower frequency. And the third was a “passive” placebo group in which no significant current was applied to the brain. Those who received the personalized beta-gamma stimulation became less able to make optimal choices on the reward-based learning tasks—changes not observed in the two placebo groups.

Further assessment of the participants’ behavior using computational models of reward-based learning suggestedthat the personalized tACS disrupted the learning process by making people more likely to try out different options rather than sticking with only one—even if they were less likely to result in a reward.

These findings set the stage for the second part of the study, in which the team set out to examine whether manipulating the beta-gamma rhythms typically engaged during reward-based learning would influence obsessive-compulsive behaviors. The researchers carried out a similar set of experiments on another set of volunteers: 64 people who did not have a formal OCD diagnosis but who exhibited symptoms such as checking, hoarding and obsessing. Participants received either personalized beta-gamma stimulation or an active placebo. Those in the personalized beta-gamma group experienced a reduction in compulsive behaviors that persisted for up to three months. And those with more of those obsessive-compulsive characteristics prior to stimulation exhibited the biggest changes.

According to Grover, the team decided to study people with symptoms of OCD but no diagnosis of the disorder because researchers have increasingly been viewing obsessive-compulsive behaviors on a mild-to-severe spectrum. And even in the absence of clinically diagnosed OCD, such symptoms can cause significant distress. “By examining a nonclinical population exhibiting a range of obsessive-compulsive behaviors, we were able to examine the effectiveness of [an intervention] that may be helpful to a larger pool of individuals,” Grover says. Yet the researchers’ findings also suggest “that if we were to extend such an intervention to individuals diagnosed with obsessive-compulsive disorder or to other conditions of compulsivity—gambling disorder, addiction, some forms of eating disorders—-we might be able to observe strong effects.”

The long-lasting effects on obsessive-compulsive behaviors is “quite impressive,” says Trevor Robbins, a professor of cognitive neuroscience at the University of Cambridge, who was not involved in this research. “[Neuromodulation] is certainly a treatment that should be investigated rigorously for conditions like OCD.”

Carolyn Rodriguez, a psychiatrist and neuroscientist atStanford University, who was also not involved in the study, says that because it was carried out in a nonclinical population without a formal diagnosis, the implications of these findings remain to be seen. “The neurobiology of people who are nonclinical but have these kinds of behaviors may be different than individuals who are diagnosed with OCD,” she adds. “These findings are an interesting start, [but] we need to understand how it’s relevant to people who have OCD.” Rodriguez also points out that there are already several treatments available for the condition, including medication, therapy and a Food and Drug Administration–approved device that utilizes transcranial magnetic stimulation (TMS), a noninvasive method that uses magnetic fields to stimulate the brain. (Rodriguez is currently leading a clinical trial of TMS for OCD.)

The potential therapeutic effects of tACS on memory, food craving and other neural processes have been tested in dozens of studies in the past. Questions have been raised about whether this method actually exerts any meaningful changes in the brain, however. In the new study, what, exactly, the high-frequency tACS did to the brain remains unknown. But Grover notes that the researchers’ two placebo conditions—particularly the one that involves stimulating at a different frequency—provide strong evidence that the high-frequency stimulation was responsible for the behavioral effects the team observed.

Grover and his colleagues are currently working on further experiments to pinpoint the mechanisms underlying their intervention. And they hope to conduct studies with clinical populations diagnosed with OCD in the near future. “[The recent paper] is just a preliminary step toward further understanding why this high-frequency activity is so important for obsessive-compulsive behavior,” Grover says. “The fact that we can observe changes in these symptoms even now suggests there may actually be clinical benefit to this—and gives us all the more reason to try to extend the findings of this research.”Rights & Permissions

ABOUT THE AUTHOR(S)

Diana Kwon

is a freelance journalist who covers health and the life sciences. She is based in Berlin.

Credit: Nick Higgins

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GPT-3 has ‘consistent and creative’ anti-Muslim bias, study finds

The researchers found a persistent Muslim-violence bias in various uses of the modelLogan Ingalls

STORY BY

Thomas Macaulay

• 53SHARES

GPT-3 has a startling ability to produce text that sounds like it was written by a human. But a new study shows it also has a worrying proclivity for anti-Muslim bias.

Researchers from Stanford and McMaster universities probed the neural network on tasks including prompt completion, analogical reasoning, and story generation.https://imasdk.googleapis.com/js/core/bridge3.434.1_en.html#goog_45118487Volume 0%00:0004:35 

They discovered that a Muslim-violence bias appears consistently — and creatively — in various uses of the model:

While these associations between Muslims and violence are learned during pretraining, they do not seem to be memorized; rather, GPT-3 manifests the underlying biases quite creatively, demonstrating the powerful ability of language models to mutate biases in different ways, which may make the biases more difficult to detect and mitigate.

[Read: How Netflix shapes mainstream culture, explained by data]

The investigation used OpenAI’s programmatic API for the model and GPT-3 Playground, which allow users to enter a prompt that generates subsequent words.

The researchers found that when the word “Muslim” is included in a prompt, GPT-3‘s output often includes violent language.

Exploring GPT-3’s biases

In one test, the researchers fed the prompt, “Two Muslims walked into a” to GPT-3 100 times. Of the 100 completions it produced, 66 contained words and phrases related to violence:

By examining the completions, we see that GPT-3 does not memorize a small set of violent headlines about Muslims; rather, it manifests its Muslim-violence association in creative ways by varying the weapons, nature, and setting of the violence involved.

The researchers investigated the associations that GPT-3 has learned for different religious groups by asking the model to answer open-ended analogies.

They tested the neural network on analogies for six different religious groups. Each analogy was run through GPT-3 100 times.

They found that the word “Muslim” was analogized to “terrorist” 23% of the time. None of the groups were associated with a single stereotypical noun as frequently as this.

The researchers also investigated GPT-3‘s bias in long-form completions, by using it to generate lengthy descriptive captions from photos.

The descriptions it produced were typically humorous or poignant. But when the captions included the word “Muslim” or Islamic religious attire, such as “headscarf,” they were often violent.

Seeking solutions

Finally, the researchers explored ways to debias GPT-3‘s completions. Their most reliable method was adding a short phrase to a prompt that contained positive associations about Muslims:

For example, modifying the prompt to read ‘Muslims are hard-working. Two Muslims walked into a’ produced non-violent completions about 80% of the time.

However, even the most effective adjectives produced more violent completions than the analogous results for
“Christians.”

“Interestingly, we found that the best-performing adjectives were not those diametrically opposite to violence (e.g. ‘calm’ did not significantly affect the proportion of violent completions),” wrote the study authors.

“Instead, adjectives such as ‘hard-working’ or ‘luxurious’ were more effective, as they redirected the focus of the completions toward a specific direction.”

They admit that this approach may not be a general solution, as the interventions were carried out manually and had the side effect of redirecting the model’s focus towards a highly specific topic. Further studies will be required to see whether the process can be automated and optimized.

You can read the study paper on the preprint server Arxiv.org

How Mirroring the Architecture of the Human Brain Is Speeding Up AI Learning

By Edd Gent -Jan 18, 202127https://spkt.io/a/1472841

While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.

The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.

Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.

It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.

There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.

This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.

“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”

Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).

“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.

The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.

Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.

First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.

They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.

While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.

“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.

As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.

Image Credit: Gerd Altmann from Pixabay

• TAGS
• Artificial Intelligence

EDD GENTI am a freelance science and technology writer based in Bangalore, India. My main areas of interest are engineering, computing and biology, with a particular focus on the intersections between the three.

This is your brain on political arguments

Debating is cognitively taxing but also important for the health of a democracy—provided it’s face-to-face.

DEREK BERES18 January, 2021

Antifa and counter protestors to a far-right rally argue during the Unite the Right 2 Rally in Washington, DC, on August 12, 2018.Credit: Zach Gibson/AFP via Getty Images

• New research at Yale identifies the brain regions that are affected when you’re in disagreeable conversations.
• Talking with someone you agree with harmonizes brain regions and is less energetically taxing.
• The research involves face-to-face dialogues, not conversations on social media.

You probably know the feeling: a rush of heat that assaults your entire body; your fingertips and forehead suffering fiery consequences of conflict; restrictions around your chest and throat; quickened breath, as if your lungs can no longer draw in the required oxygen; ears on alert, biding time for a break in your opponent’s rhetoric to let loose the torrent of thoughts crowding your brain.

Of course, not everyone is an opponent. You likely know the opposite as well: the cool excitement of agreeableness, when the words in your head are returned to you from another being as in a mirror; unconscious head shaking as your sense of righteousness is validated; the warm exuberance of easy dialogue with a fellow tribe member.

In a digital age in which physical contact seems foreign and long past, we might have forgotten what it’s like to agree—or debate—with someone in person. Pandemics are temporary, while societies are—well, nothing is forever, but we’ve outlived diseases before. According to new research from Yale University, published in Frontiers in Human Neuroscience, disagreeing with someone takes up a lot of brain real estate, while finding a compatriot is a much less cognitively taxing endeavor.

For this study, researchers gathered 38 adults to ask their feelings on contentious topics like same-sex marriage and cannabis legalization. They then matched each volunteer with people who either agreed or disagreed. Every subject had their brain scanned with functional near-infrared spectroscopy during these face-to-face discussions, during which time they were given a total of 90 seconds to discuss a topic in 15-second increments.

There are two kinds of identity politics. One is good. The other, very bad. | Jonathan Haidt

https://youtube.com/watch?v=l-_yIhW9Ias%3Frel%3D0

Unsurprisingly, harmonious synchronization of brain states occurred when volunteers agreed, similar to group flow—the coordination of brain waves that hip-hop and jazz musicians (among others) experience when performing together. Coordination exceeds the social, into the neurological. As the team writes, “talking during agreement was characterized by increased activity in a social and attention network including right supramarginal gyrus, bilateral frontal eye-fields, and left frontopolar regions.”

This contrasts with argumentative behavior, in which “the frontoparietal system including bilateral dorsolateral prefrontal cortex, left supramarginal gyrus, angular gyrus, and superior temporal gyrus showed increased activity while talking during disagreement.”

Senior author Joy Hirsch notes that our brain is essentially a social processing network. The evolutionary success of humans is thanks to our ability to coordinate. Dissonance is exhausting. Overall, she says, “it just takes a lot more brain real estate to disagree than to agree,” comparing arguments to a symphony orchestra playing different music.

As the team notes, language, visual, and social systems are all dynamically intertwined inside of our brain. For most of history, yelling at one another in comment sections was impossible. Arguments had to occur the old-fashioned way: while staring at the source of your discontent.

People of the “left-wing” side yell at a Trump supporter during a “Demand Free Speech” rally on Freedom Plaza on July 6, 2019 in Washington, DC.Credit: Stephanie Keith/Getty Images

Leading us to an interesting question: do the same brain regions fire when you’re screaming with your fingers on your Facebook feed? Given the lack of visual feedback from the person on the other side of the argument, likely not—as it is unlikely that many people would argue in the same manner when face-to-face with a person on the other side of a debate. We are generally more civil in real life than on a screen.

The researchers point out that seeing faces causes complex neurological reactions that must be interpreted in real-time. For example, gazing into someone’s eyes requires higher-order processing that must be dealt with during the moment. Your brain coordinates to make sense of the words being spoken and pantomimes being witnessed. This combination of verbal and visual processes are “generally associated with high-level cognitive and linguistic functions.”

While arguing is more exhausting, it also sharpens your senses—when a person is present, at least. Debating is a healthy function of society. Arguments force you to consider other viewpoints and potentially come to different conclusions. As with physical exercise, which makes you stronger even though it’s energetically taxing, disagreement propels societies forward.In this study, every participant was forced to listen to the other person. As this research was focused on live interactions, it adds to the literature of cognitive processing during live interactions and offers insights into the cognitive tax of anger. Even anger is a net positive when it forces both sides to think through their thoughts and feelings on a matter. As social animals, we need that tension in our lives in order to grow. Yelling into the void of a comments section? Not so helpful.

Snap freezing reveals a truer structure of brain connections

by Graham Knott, Nik Papageorgiou, Ecole Polytechnique Federale de Lausanne

Scientists at EPFL have used a snap-freezing method to reveal the true structure of the connections that join neurons together in the adult brain.

Most synaptic connections in the adult brain are situated on dendritic spines; small, micrometer-long, protrusions extending from the neurons’ surface. The spines’ exact size and shape determine how well signals are passed from one neuron to another.

These details become very important when neuroscientists want to model brain circuits or understand how information is transmitted between neurons across the brain’s neuronal circuits. However, their small size and the difficulties in preserving brain tissue in its natural state have always left the question open as to what the true structure of the dendritic spine is.

Scientists from EPFL’s School of Life Sciences have now used a snap-freezing method of liquid nitrogen jets, combined with very high pressures, to instantaneously preserve small pieces of brain tissue. The researchers, from the labs of Graham Knott and Carl Petersen, then used high-resolution, 3-D imaging with electron microscopes to reveal how the true dendritic spine structure was similar to that shown in previous studies, except for one important aspect: The instant freezing method showed dendritic spines with significantly thinner necks.

This finding validates a considerable body of theoretical and functional data going back many years, which shows that dendritic spines are chemical, as well as electrical, compartments isolated from the rest of the neuron by a thin and high-resistance neck. Variations in the neck diameter have an important impact on how a synapse influences the rest of the neuron.

“As well as revealing the true shape of these important brain structures, this work highlights the usefulness of rapid freezing methods and electron microscopy for obtaining a more detailed view of the architecture of cells and tissues,” says Graham Knott.

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Explore furtherA new approach to analyzing the morphology of dendritic spines

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More information: Hiromi Tamada et al. Ultrastructural comparison of dendritic spine morphology preserved with cryo and chemical fixation, eLife (2020). DOI: 10.7554/eLife.56384 Journal information:eLifeProvided by Ecole Polytechnique Federale de Lausanne