What to know about circadian rhythm

• What it is
• How it works
• What affects it?
• Possible dusruptions
• How to maintain
• Contacting a doctor
• Summary

Circadian rhythms are cycles in the body that occur roughly across 24 hours. In humans, circadian rhythms cause physical and mental changes in the body, including feelings of wakefulness and sleep.

However, several issues may alter these circadian rhythms, which could lead to sleep disruptions or other health issues.

Keep reading to learn more, including how it works, factors that may disrupt it, and some tips on maintaining a healthful circadian rhythm.

What is a circadian rhythm?

A circadian rhythm is a natural process that takes place throughout every day. These rhythms take place everywhere, occurring throughout the natural world, such as in plants and other animals. They are essential to organisms and occur even in the absence of outside factors.

In humans, circadian rhythms are the approximate 24-hour patterns the body and brain go through, allowing for changes in the body’s physical and mental states, along with mood and behavioral changes.

The sleep-wake cycle is one of the most widely recognized circadian rhythms. Humans tend to become tired at night and feel more awake during the day. This 24-hour pattern is what most people refer to when they talk about a circadian rhythm. However, they encompass factors other than sleep.

Other examples of circadian rhythms in humans include:

• hormonal activity
• body temperature
• digestion
• immune function

How does it work?

Circadian rhythms are vital processes that function without external factors. This is because the body itself responds to biological clocks, which exist naturally in humans and their cells.

The National Institute of General Medical Sciences note that nearly every tissue and organ contain their own biological clocks. These are the result of certain proteins interacting with cells in the body, instructing them to be more active or to slow down.

One master clock in the body controls all these individual clocks. In humans, the master clock is a structure called the suprachiasmatic nucleus (SCN), which contains about 20,000 nerve cells and receives direct input from the eyes.

As the eyes perceive the bright light of day or the darkness of night, the SCN picks up on this information, telling the cells to act accordingly. Light keeps the circadian rhythm in sync with a 24-hour day.

In addition to reactions in the cells themselves, chemicals in the brain adjust in response to the cycles of the day.

These chemicals adjust a number of factors in the body, such as:

• hunger
• temperature
• arousal and awakeness
• mood

How does it relate to sleep?

The body’s circadian rhythms control the sleep-wake cycle. They play a role in sleep due to how the body and brain respond to darkness, which is when most humans feel tired and tend to sleep.

As darkness sets in, the body’s biological clock instructs the cells to slow down. When the evening becomes dark, the hormone melatonin starts to rise and allows sleep to occur. Melatonin peaks around 2–4 A.M. and then reduces by morning, allowing wakefulness.

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What affects circadian rhythm?

Light is the major outside factor controlling the body’s circadian rhythms. It keeps the circadian rhythm in sync with the Earth’s natural 24-hour cycle. In addition, other environmental cues may help synchronize the circadian rhythm, including food intake and activity level. However, many things can disrupt this process.

What can disrupt them?

While circadian rhythms occur naturally, several factors may affect them across the day.

Light

Irregularly-timed light can easily disrupt a normal circadian rhythm.

The Centers for Disease Control and Prevention (CDC) note that the circadian clock is most sensitive around 2 hours before a person’s usual bedtime. Using bright lights during this time can shift the need to sleep later, so a person may get sleepy and fall asleep later in the evening and wake up later in the morning.

In contrast, bright morning light can shift the need for sleep earlier. Sleeping in a bright room may also wake a person up earlier than necessary and displace their usual sleep time.

Color

The color of lights appears to disrupt circadian patterns. The CDC note that blue wavelength light has the strongest impact.

Blue and white lights during sensitive periods of the day, such as 2 hours before bed, can make it difficult for a person to fall asleep or stay asleep. Common sources include electronic screens on devices such as phones, computers, and televisions.

Other wavelengths of light have less effect on the circadian clock.

Unhealthful sleep habits

Having unhealthful sleep habits may disrupt the circadian clock across the day. This may include issues such as:

• going out late and waking up early
• having no set sleep time
• eating and drinking late at night
• consuming caffeine late at night
• using electronic devices late at night
• performing mentally stimulating activities late in the day
• having pain or discomfort in the sleeping space

Read about some tips and remedies for improving the quality of sleep here.

Shift work

People who work late shifts or work throughout the night may experience disruptions in their natural circadian rhythms. As the body responds to the sun’s natural light and dark cycles, shift work changes their circadian rhythms.

Travel

People who travel frequently may experience disruptions in sleep and their circadian rhythms, especially if they often move between time zones. This is known as jet lag, the groggy or tired feeling as the body tries to catch up with time changes and the new rhythms of the day.

Read about some tips for getting over jet lag here.

Underlying conditions

Underlying sleep disorders may affect circadian rhythms, including:

• Delayed sleep phase syndrome: When a person’s circadian rhythm becomes delayed, so they prefer to fall asleep and wake later.
• Advanced sleep phase syndrome: The circadian rhythm is advanced, so a person feels sleepy earlier in the evening and wakes earlier in the morning.
• Irregular sleep-wake disorder: There is a lack of regular rhythm that leads to sleep and waking disruptions.
• Non-24 hour sleep-wake disorder: The circadian rhythm is not synchronized to a 24-hour day, resulting in periods of sleepiness and periods of insomnia.

Learn more about some of the other conditions that may lead to difficulty sleeping here.MEDICAL NEWS TODAY NEWSLETTERStay in the know. Get our free daily newsletter

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How to maintain a healthful circadian rhythm

There are several important factors to consider when maintaining a healthful circadian rhythm.

If possible, go to bed and wake up at the same time each day. Setting a regular time may help the body set its rhythms around these times. Some choose to set a morning alarm to wake up at the same time each day. This may help the body adjust and encourage tiredness when they need to sleep to wake up on time.

This regular sleep-wake schedule also includes days off from work, such as weekends.

As light can disrupt the circadian rhythms, it is important to choose when to limit exposure. The CDC note that the 2 hours before a person falls asleep appear to be most crucial. Avoiding blue light at this time may help ensure a regular circadian rhythm, which includes limiting screen time and any bright sources of white or blue light, such as in shops.

Other tips may help promote a healthful circadian rhythm, including:

• going outside or in bright light in the morning
• avoiding caffeine late in the day
• taking small naps in the early afternoon if a person needs to nap
• avoiding long naps or napping later in the day
• avoiding heavy meals
• performing calming activities before bed, such as reading or doing gentle stretches

Some calming herbal teas or supplements may help promote a sleepy state in people with trouble falling asleep. However, talk with a doctor before taking products with active ingredients.

When to contact a doctor

While it is normal to feel groggy at times, anyone who regularly experiences sleep disruptions or feels their circadian rhythms are off may want to talk with their doctor.

Poor quality sleep or sleep deprivation can lead to health complications, including:

• hypertension
• diabetes or insulin resistance
• sleep apnea
• obesity
• heart attack
• stroke
• depression and anxiety
• psychosis

Learn more about the negative effects of sleep deprivation here.

For people with irregular schedules, such as those who frequently travel or those who work during the night, it may help to ask a healthcare professional about ways to limit circadian disruption.

Melatonin may help bring on sleep and reset the circadian rhythms, but it is important to use it correctly. Talk with a doctor before using hormones to reset a sleep cycle.

Summary

Circadian rhythms are natural cycles the body goes through each day. The rhythm of sleep and wakefulness is the most widely recognized example of these rhythms.

Maintaining a healthful circadian rhythm may involve adjusting a person’s habits to match the rhythms of nature, and may help prevent some issues with sleeping or waking.

Anyone uncertain about their symptoms should speak with a doctor for a full diagnosis and management plan.

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Last medically reviewed on January 11, 2021

“INSOMNIA-LIKE” SLEEP PATTERNS CAN PREDICT FUTURE STRESS

New research suggests fragmented sleep patterns contain a critical message.Getty ImagesEMMA BETUEL21 HOURS AGO

A BAD NIGHT’S SLEEP COMES WITH A HOST OF CONSEQUENCES, some of which aren’t obvious right away. This is especially true when stressful situations — like a year defined by a pandemic — hit. Research suggests specific abnormal sleep patterns may decrease one skill that’s crucial to weathering the storm.

In a mouse study published Tuesday in Frontiers in Neuroscience, scientists found fragmented sleep patterns – a pattern of sleep marked by more awakenings and shorter bouts of non-rapid eye movement sleep –could predict how mice responded to future stress. Mice with regular sleep patterns were resilient to bullying, while those with fragmented sleep patterns weren’t equipped to deal with the abuse.

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Dipesh Chaudhury is the study’s lead author and an assistant professor of biology at New York University Abu Dhabi. He tells Inverse that this study deepens our understanding of how stress and sleep are related. Typically, we assume that stress leads to poor sleep. But things could also work the other way around, with poor sleep dampening resilience to stress at the same time.

“Our findings also indicate that those mice that exhibit abnormal sleep prior to stress are more sensitive to future stress exposure. In other words, sleep abnormalities can also be a cause of stress-related disorders,” Chaudhury says.

WHAT DOES A “DISRUPTED SLEEP PATTERN LOOK LIKE? — Chaudhury’s study was based on the sleep and stress patterns of 22 mice who had electrodes implanted into their brains. Those electrodes could measure the amount of time each mouse spent in each stage of sleep.https://bc6fa435e49a93271733b89dc9040d70.safeframe.googlesyndication.com/safeframe/1-0-37/html/container.html

Like humans, mice move through sleep stages including rapid eye movement sleep (REM) — when most dreaming happens in humans — and lighter, non-REM stages.

Every mouse was exposed to chronic social defeat — 15 consecutive days of being attacked by aggressive mice identified at the outset of the study. The researchers found that they could break the mice up into two groups: mice who were resilient, bouncing back from bullying ready to socialize, and ones that succumbed, and retreated from others.

WHAT WAS DISCOVERED — The scientists found there were significant differences in the sleep patterns of each group of mice beforethey were exposed to the bullying.

The mice in the non-resilient group showed signs of fragmented non-REM sleep. They woke up more during sleep periods (mice sleep during the day) and had were shorter NREM bouts, on average, than those seen in the resilient mice.

“In essence, the susceptible mice exhibit insomnia-like traits even before exposure to stress,” Chaudry says.

Ultimately, these patterns could predict which group the mice ended up in with about 80 percent accuracy, suggesting that it could have been one reason they were less resilient when stress took hold.

WHAT DOES THIS MEAN FOR HUMANS? – This study taps into a robust area of research on human mood disorders. Depression and sleep are intertwined in a way that makes it hard to distinguish cause and effect. Disturbed sleep is often seen in depressive patients, but those who experience insomnia are also more likely to develop depression in the first place.

Though this study focuses on stress, it suggests that sleep may pose an even more central role in mood disorders – or in this case, resilience to stress. Before the stressful situation occurs, sleep patterns may contain early warning signs.

These results are early-stage, Chaudhury cautions. A mouse brain (and a mouse’s stress) is quite different from a human’s. But he is optimistic that the idea that sleep signatures could be a prediction of stress — specifically the way the brain transitions in and out of non-REM sleep — will hold true in humans.https://bc6fa435e49a93271733b89dc9040d70.safeframe.googlesyndication.com/safeframe/1-0-37/html/container.html

He imagines monitoring the sleep patterns of people who have particularly high-stress jobs, like first responders or frontline workers. Even if signs of stress have yet to show themselves, a disrupted sleep pattern could be the sign of a vicious cycle (stress leading to worse sleep, leading to less resilience to stress) about to go awry.

“By having simple non-invasive markers of stress susceptibility, such as EEG sleep patterns, it may be possible to develop strategies to protect the more vulnerable people,” Chaudhury says.

Abstract: There is a tight association between mood and sleep as disrupted sleep is a core feature of many mood disorders. The paucity in available animal models for investigating the role of sleep in the etiopathogenesis of depression-like behaviors led us to investigate whether prior sleep disturbances can predict susceptibility to future stress. Hence, we assessed sleep before and after chronic social defeat (CSD) stress. The social behavior of the mice post stress was classified in two main phenotypes: mice susceptible to stress that displayed social avoidance and mice resilient to stress. Pre-CSD, mice susceptible to stress displayed increased fragmentation of Non-Rapid Eye Movement (NREM) sleep, due to increased switching between NREM and wake and shorter average duration of NREM bouts, relative to mice resilient to stress. Logistic regression analysis showed that the pre-CSD sleep features from both phenotypes were separable enough to allow prediction of susceptibility to stress with >80% accuracy. Post-CSD, susceptible mice maintained high NREM fragmentation while resilient mice exhibited high NREM fragmentation, only in the dark. Our findings emphasize the putative role of fragmented NREM sleep in signaling vulnerability to stress.

Google trained a trillion-parameter AI language model

Kyle Wiggers@Kyle_L_WiggersJanuary 12, 2021 10:36 AMAI

NEW YORK, NEW YORK – OCTOBER 20: Google’s offices stand in downtown Manhattan on October 20, 2020 in New York City. Accusing the company of using anticompetitive tactics to illegally monopolize the online search and search advertising markets, the Justice Department and 11 states Tuesday filed an antitrust case against Google.Image Credit: Spencer Platt/Getty Images

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Parameters are the key to machine learning algorithms. They’re the part of the model that’s learned from historical training data. Generally speaking, in the language domain, the correlation between the number of parameters and sophistication has held up remarkably well. For example, OpenAI’s GPT-3 — one of the largest language models ever trained, at 175 billion parameters — can make primitive analogies, generate recipes, and even complete basic code.

In what might be one of the most comprehensive tests of this correlation to date, Google researchers developed and benchmarked techniques they claim enabled them to train a language model containing more than a trillion parameters. They say their 1.6-trillion-parameter model, which appears to be the largest of its size to date, achieved an up to 4 times speedup over the previously largest Google-developed language model (T5-XXL).28KPlayUnmuteDuration 1:33Toggle Close Captions/Current Time 0:14Loaded: 21.36% FullscreenUp Nexthttps://imasdk.googleapis.com/js/core/bridge3.433.1_en.html#goog_245347483https://imasdk.googleapis.com/js/core/bridge3.433.1_en.html#goog_538040948

As the researchers note in a paper detailing their work, large-scale training is an effective path toward powerful models. Simple architectures, backed by large datasets and parameter counts, surpass far more complicated algorithms. But effective, large-scale training is extremely computationally intensive. That’s why the researchers pursued what they call the Switch Transformer, a “sparsely activated” technique that uses only a subset of a model’s weights, or the parameters that transform input data within the model.ADVERTISEMENT

The Switch Transformer builds on a mix of experts, an AI model paradigm first proposed in the early ’90s. The rough concept is to keep multiple experts, or models specialized in different tasks, inside a larger model and have a “gating network” choose which experts to consult for any given data.

The novelty of the Switch Transformer is that it efficiently leverages hardware designed for dense matrix multiplications — mathematical operations widely used in language models — such as GPUs and Google’s tensor processing units (TPUs). In the researchers’ distributed training setup, their models split unique weights on different devices so the weights increased with the number of devices but maintained a manageable memory and computational footprint on each device.

In an experiment, the researchers pretrained several different Switch Transformer models using 32 TPU cores on the Colossal Clean Crawled Corpus, a 750GB-sized dataset of text scraped from Reddit, Wikipedia, and other web sources. They tasked the models with predicting missing words in passages where 15% of the words had been masked out, as well as other challenges, like retrieving text to answer a list of increasingly difficult questions.

The researchers claim their 1.6-trillion-parameter model with 2,048 experts (Switch-C) exhibited “no training instability at all,” in contrast to a smaller model (Switch-XXL) containing 395 billion parameters and 64 experts. However, on one benchmark — the Sanford Question Answering Dataset (SQuAD) — Switch-C scored lower (87.7) versus Switch-XXL (89.6), which the researchers attribute to the opaque relationship between fine-tuning quality, computational requirements, and the number of parameters.ADVERTISEMENT

This being the case, the Switch Transformer led to gains in a number of downstream tasks. For example, it enabled an over 7 times pretraining speedup while using the same amount of computational resources, according to the researchers, who demonstrated that the large sparse models could be used to create smaller, dense models fine-tuned on tasks with 30% of the quality gains of the larger model. In one test where a Switch Transformer model was trained to translate between over 100 different languages, the researchers observed “a universal improvement” across 101 languages, with 91% of the languages benefitting from an over 4 times speedup compared with a baseline model.

“Though this work has focused on extremely large models, we also find that models with as few as two experts improve performance while easily fitting within memory constraints of commonly available GPUs or TPUs,” the researchers wrote in the paper. “We cannot fully preserve the model quality, but compression rates of 10 to 100 times are achievable by distilling our sparse models into dense models while achieving ~30% of the quality gain of the expert model.”

In future work, the researchers plan to apply the Switch Transformer to “new and across different modalities,” including image and text. They believe that model sparsity can confer advantages in a range of different media, as well as multimodal models.

Unfortunately, the researchers’ work didn’t take into account the impact of these large language models in the real world. Models often amplify the biases encoded in this public data; a portion of the training data is not uncommonly sourced from communities with pervasive gender, race, and religious prejudices. AI research firm OpenAI notes that this can lead to placing words like “naughty” or “sucked” near female pronouns and “Islam” near words like “terrorism.”  Other studies, like one published in April by Intel, MIT, and Canadian AI initiative CIFAR researchers, have found high levels of stereotypical bias from some of the most popular models, including Google’s BERT and XLNet, OpenAI’s GPT-2, and Facebook’s RoBERTa. This bias could be leveraged by malicious actors to foment discord by spreading misinformation, disinformation, and outright lies that “radicalize individuals into violent far-right extremist ideologies and behaviors,” according to the Middlebury Institute of International Studies.

It’s unclear whether Google’s policies on published machine learning research might have played a role in this. Reuters reported late last year that researchers at the company are now required to consult with legal, policy, and public relations teams before pursuing topics such as face and sentiment analysis and categorizations of race, gender, or political affiliation. And in early December, Google fired AI ethicist Timnit Gebru, reportedly in part over a research paper on large language models that discussed risks, including the impact of their carbon footprint on marginalized communities and their tendency to perpetuate abusive language, hate speech, microaggressions, stereotypes, and other dehumanizing language aimed at specific groups of people.

The Best Essential Oils For Sleep, According to Experts

January 11, 2021by CAITLIN FLYNN50 SharesView On One PageStart Slideshow 

← USE ARROW KEYS →Image Source: Unsplash / Christin Hume

In any given year, one in four American adults experiences insomnia. As anyone who has ever spent the night tossing and turning knows, it’s seriously unpleasant to go about your day after a night of insufficient sleep. Sleep deprivation can cause headaches, mood swings, and trouble concentrating.

Doctors sometimes prescribe sleep aids as a quick fix, but these medications can have serious side effects. A far better solution is to work on developing healthy sleep habits and a relaxing bedtime routine, which might include meditation, reading, and, yes, essential oils. There’s no shortage of essential oils out there, but doctors say certain oils are beneficial to sleep and relaxation. These are the four the experts POPSUGAR spoke with recommend most.

CES 2021: This hearing aid uses AI to bring more sounds to your ears

The Oticon More hearing aid is built with an onboard deep neural network that creates a fuller, more balanced hearing experience.

Alison DeNisco RayomeJan. 12, 2021 9:36 a.m. PT

LISTEN- 01:28

This story is part of CES, where our editors will bring you the latest news and the hottest gadgets of the entirely virtual CES 2021.

At CES 2021 on Tuesday, hearing aid manufacturer Oticon, Inc. launched its Oticon More hearing aid — the first built with an onboard deep neural network that gives people with hearing loss a better experience, the company claims. It joins a growing number of other hearing assistance devices that use technology to augment hearing. 

The deep neural network inside the hearing aid is trained on 12 million real-life sounds, meaning it can process speech in noise more like the human brain does, and gives the brain more information needed to hear sonic details. It’s built for people with mild to severe hearing loss. Oticon says the new hearing aid improves wearers’ speech understanding, reduces the effort needed to listen and helps them remember more of what is being said, even when there’s a lot of background noise. 

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Oticon More scans and analyzes the sound scene around you 500 times per second, and captures and processes all of the details of sound with more clarity and balance than others on the market, according to a release. 

The hearing aid has a rechargeable battery. It can connect to your iPhone or Android device for streaming or making phone calls. And the Oticon ON app lets you control the device from your smartphone, adjusting the volume, checking the battery level and tracking your aid if you lose it. 

Oticon More was named a CES 2021 Innovation Awards Honoree in the Health & Wellness and Wearable Technologies categories. It’s available to purchase today, and its price is determined by individual hearing care professionals.  

For more, check out CES 2021’s coolest new gadgets: Rollable phones, giant TVs, coronavirus killers. 

New method helps pocket-sized DNA sequencer achieve near-perfect accuracy

by University of British Columbia

Researchers have found a simple way to eliminate almost all sequencing errors produced by a widely used portable DNA sequencer, potentially enabling scientists working outside the lab to study and track microorganisms like the SARS-CoV-2 virus more efficiently.

Using special molecular tags, the team was able to reduce the five-to-15 percent error rate of Oxford Nanopore Technologies’ MinION device to less than 0.005 percent—even when sequencing many long stretches of DNA at a time.

“The MinION has revolutionized the field of genomics by freeing DNA sequencing from the confines of large laboratories,” says Ryan Ziels, an assistant professor of civil engineering at the University of British Columbia and the co-lead author of the study, which was published this week in Nature Methods. “But until now, researchers haven’t been able to rely on the device in many settings because of its fairly high out-of-the-box error rate.”

Genome sequences can reveal a great deal about an organism, including its identity, its ancestry and its strengths and vulnerabilities. Scientists use this information to better understand the microbes living in a particular environment, as well as to develop diagnostic tools and treatments. But without accurate portable DNA sequencers, crucial genetic details could be missed when research is conducted out in the field or in smaller laboratories.

So Ziels and his collaborators at Aalborg University created a unique barcoding system that can make long-read DNA sequencing platforms like the MinION over 1000 times more accurate. After tagging the target molecules with these barcodes, researchers proceed as they usually would—amplifying, or making multiple copies of, the tagged molecules using the standard PCR technique and sequencing the resulting DNA.

The researchers can then use the barcodes to easily identify and group relevant DNA fragments in the sequencing data, ultimately producing near-perfect sequences from fragments that are up to 10 times longer than conventional technologies can process. Longer stretches of DNA allow the detection of even slight genetic variations and the assembly of genomes in high resolution.

“A beautiful thing about this method is that it is applicable to any gene of interest that can be amplified,” says Ziels, whose team has made the code and protocol for processing the sequencing data available through open-source repositories. “This means that it can be very useful in any field where the combination of high-accuracy and long-range genomic information is valuable, such as cancer research, plant research, human genetics and microbiome science.”

Ziels is currently collaborating with Metro Vancouver to develop an expanded version of the method that permits the near-real-time detection of microorganisms in water and wastewater. With an accurate picture of the microorganisms present in their water systems, says Ziels, communities may be able to improve their public health strategies and treatment technologies—and better control the spread of harmful microorganisms like SARS-CoV-2.

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Explore furtherNew DNA scanning method could lead to quicker diagnosis of cancer and rare disease

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More information: Søren M. Karst et al, High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing, Nature Methods (2021). DOI: 10.1038/s41592-020-01041-yJournal information:Nature MethodsProvided by University of British Columbia

Our Brains Predict Every Sound We Hear

NEWS   Jan 11, 2021 | Original story from TU Dresden

Credit: Photo by Jessica Flavia on Unsplash Read Time: 3 min

Humans depend on their senses to perceive the world, themselves and each other. Despite senses being the only window to the outside world, people do rarely question how faithfully they represent the external physical reality. During the last 20 years, neuroscience research has revealed that the cerebral cortex constantly generates predictions on what will happen next, and that neurons in charge of sensory processing only encode the difference between our predictions and the actual reality.

A team of neuroscientists of TU Dresden headed by Prof Katharina von Kriegstein presents new findings that show that not only the cerebral cortex, but the entire auditory pathway, represents sounds according to prior expectations.

For their study, the team used functional magnetic resonance imaging (fMRI) to measure brain responses of 19 participants while they were listening to sequences of sounds. The participants were instructed to find which of the sounds in the sequence deviated from the others. Then, the participants’ expectations were manipulated so that they would expect the deviant sound in certain positions of the sequences. The neuroscientists examined the responses elicited by the deviant sounds in the two principal nuclei of the subcortical pathway responsible for auditory processing: the inferior colliculus and the medial geniculate body. Although participants recognised the deviant faster when it was placed on positions where they expected it, the subcortical nuclei encoded the sounds only when they were placed in unexpected positions.

These results can be best interpreted in the context of predictive coding, a general theory of sensory processing that describes perception as a process of hypothesis testing. Predictive coding assumes that the brain is constantly generating predictions about how the physical world will look, sound, feel, and smell like in the next instant, and that neurons in charge of processing our senses save resources by representing only the differences between these predictions and the actual physical world.

Dr Alejandro Tabas, first author of the publication, states on the findings: “Our subjective beliefs on the physical world have a decisive role on how we perceive reality. Decades of research in neuroscience had already shown that the cerebral cortex, the part of the brain that is most developed in humans and apes, scans the sensory world by testing these beliefs against the actual sensory information. We have now shown that this process also dominates the most primitive and evolutionary conserved parts of the brain. All that we perceive might be deeply contaminated by our subjective beliefs on the physical world.”

These new results open up new ways for neuroscientists studying sensory processing in humans towards the subcortical pathways. Perhaps due to the axiomatic belief that subjectivity is inherently human, and the fact that the cerebral cortex is the major point of divergence between the human and other mammal’s brains, little attention has been paid before to the role that subjective beliefs could have on subcortical sensory representations.

Given the importance that predictions have on daily life, impairments on how expectations are transmitted to the subcortical pathway could have profound repercussion in cognition. Developmental dyslexia, the most wide-spread learning disorder, has already been linked to altered responses in subcortical auditory pathway and to difficulties on exploiting stimulus regularities in auditory perception. The new results could provide with a unified explanation of why individuals with dyslexia have difficulties in the perception of speech, and provide clinical neuroscientists with a new set of hypotheses on the origin of other neural disorders related to sensory processing.

Reference:

Tabas A, Mihai G, Kiebel S, Trampel R, von Kriegstein K. Abstract rules drive adaptation in the subcortical sensory pathway. Shinn-Cunningham BG, Griffiths TD, Malmierca MS, eds. eLife. 2020;9:e64501. doi:10.7554/eLife.64501

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

Higher Ed IT Teams Adapt Back-Office Operations to Remote Work

Colleges rethink legacy systems and adapt new solutions to optimize operating models.

byCraig Guillot

Craig Guillot is a business writer based in New Orleans. He specializes in technology and writes about IoT, cybersecurity and SaaS for trade publications and tech companies.LISTEN03:58

The shift to remote operations has been transformative for learning, but educational institutions are also feeling the effects in their back-office operations — leveraging new solutions and in some cases tackling upgrades that previously had not been priorities.

Just as colleges have adopted new tools and policies for online learning in recent months, they have a similar opportunity to achieve new efficiencies and deepen resiliency on the business and operations side.

The closure of in-person education in March forced educational institutions to change their back-office operations overnight, says Dave Ballard, senior vice president for public sector for SAP Concur. Because few had the infrastructure to go virtual, most had to improvise, quickly piecing together a mix of tools and strategies.

“The back-office changes and operational issues caught people off guard,” Ballard says. “Many didn’t have the level of preparedness around technology and business continuity they needed.”

Remote Operations Add Complexity to Higher Ed IT

In August, SAP Concur surveyed more than 500 finance and administration decision-makers in higher education institutions and K–12 schools across the country about how they are navigating hybrid and remote work and learning environments. One key finding was that remote work has made administrative roles more challenging and complex.

More than 40 percent of respondents said they were managing at least three new responsibilities since the start of the pandemic. In addition, almost all said that sustained remote work was impairing their ability to comply with state and federal government reporting regulations.

Although some institutions traditionally have been hesitant to upgrade legacy systems and processes, the pandemic left them little choice, says Ballard. Nearly all respondents said investments in technology or personnel would be critical in maintaining operations. Seventy percent said that investments in hardware and software are essential to facilitate department operations that are remote, but 63 percent said their finance and administrative departments lack a fully remote solution.

MORE FROM EDTECH: Get best practices for choosing the right IT partner.

Colleges Seize Opportunities to Upgrade Administrative Functions

Despite these challenges, the forced adoption of new workflows has created opportunities for institutions to enhance their resiliency and efficiency, says Ballard. One area in which many have been able to justify increased spending is in business continuity and back-office automation. SAP Concur, for example, has seen an uptick in interest in its invoicing application, says Ballard.

“We saw one school where people were driving to the CFO’s house to get checks cut to pay suppliers,” he says. “Many are now really looking into accounts payable automation.”

Even when in-person classes resume, campus leaders indicate that they expect remote work and distance learning to continue to some degree, thereby continuing to affect their operating models.

In the survey, half of the higher education decision-makers said they expect to adopt new operating models in the next five years to offset revenue losses. Among this group, 94 percent also anticipate cost-cutting measures such as hiring freezes, layoffs and pay cuts. Despite budget cuts and workforce reductions, many are now adopting mobile apps and tools to help them migrate to hybrid classroom and back-office operation models.

“There has been a huge shift in mindset,” says Ballard. “We had people running on systems so siloed and dated they were afraid to make changes. Now, the cracks have become chasms in their operations, and they’re finally starting to address it.”