Showing posts with label Memory and learning. Show all posts
Showing posts with label Memory and learning. Show all posts

Thursday, 3 June 2021

Sleep Evolved Before Brains. Hydras Are Living Proof.

Sleep 

 

 Studies of sleep are usually neurological. But some of nature’s simplest animals suggest that sleep evolved for metabolic reasons, long before brains even existed.

 

 

The hydra is a simple creature. Less than half an inch long, its tubular body has a foot at one end and a mouth at the other. The foot clings to a surface underwater — a plant or a rock, perhaps — and the mouth, ringed with tentacles, ensnares passing water fleas. It does not have a brain, or even much of a nervous system.

And yet, new research shows, it sleeps. Studies by a team in South Korea and Japan showed that the hydra periodically drops into a rest state that meets the essential criteria for sleep.

On the face of it, that might seem improbable. For more than a century, researchers who study sleep have looked for its purpose and structure in the brain. They have explored sleep’s connections to memory and learning. They have numbered the neural circuits that push us down into oblivious slumber and pull us back out of it. They have recorded the telltale changes in brain waves that mark our passage through different stages of sleep and tried to understand what drives them. Mountains of research and people’s daily experience attest to human sleep’s connection to the brain.

But a counterpoint to this brain-centric view of sleep has emerged. Researchers have noticed that molecules produced by muscles and some other tissues outside the nervous system can regulate sleep. Sleep affects metabolism pervasively in the body, suggesting that its influence is not exclusively neurological. And a body of work that’s been growing quietly but consistently for decades has shown that simple organisms with less and less brain spend significant time doing something that looks a lot like sleep. Sometimes their behavior has been pigeonholed as only “sleeplike,” but as more details are uncovered, it has become less and less clear why that distinction is necessary.

It appears that simple creatures — including, now, the brainless hydra — can sleep. And the intriguing implication of that finding is that sleep’s original role, buried billions of years back in life’s history, may have been very different from the standard human conception of it. If sleep does not require a brain, then it may be a profoundly broader phenomenon than we supposed.

Recognizing Sleep

Sleep is not the same as hibernation, or coma, or inebriation, or any other quiescent state, wrote the French sleep scientist Henri Piéron in 1913. Though all involved a superficially similar absence of movement, each had distinctive qualities, and that daily interruption of our conscious experience was particularly mysterious. Going without it made one foggy, confused, incapable of clear thought. For researchers who wanted to learn more about sleep, it seemed essential to understand what it did to the brain.

And so, in the mid-20th century, if you wanted to study sleep, you became an expert reader of electroencephalograms, or EEGs. Putting electrodes on humans, cats or rats allowed researchers to say with apparent precision whether a subject was sleeping and what stage of sleep they were in. That approach produced many insights, but it left a bias in the science: Almost everything we learned about sleep came from animals that could be fitted with electrodes, and the characteristics of sleep were increasingly defined in terms of the brain activity associated with them.

This frustrated Irene Tobler, a sleep physiologist working at the University of Zurich in the late 1970s, who had begun to study the behavior of cockroaches, curious whether invertebrates like insects sleep as mammals do. Having read Piéron and others, Tobler knew that sleep could be defined behaviorally too.

She distilled a set of behavioral criteria to identify sleep without the EEG. A sleeping animal does not move around. It is harder to rouse than one that’s simply resting. It may take on a different pose than when awake, or it may seek out a specific location for sleep. Once awakened it behaves normally rather than sluggishly. And Tobler added a criterion of her own, drawn from her work with rats: A sleeping animal that has been disturbed will later sleep longer or more deeply than usual, a phenomenon called sleep homeostasis.

A figure showing some of the body postures of cockroaches, which the researcher Irene Tobler used as diagnostics for sleep in the insects.

Courtesy of Irene Tobler

Tobler soon laid out her case that cockroaches were either sleeping or doing something very like it. The response from her colleagues, most of whom studied higher-order mammals, was immediate. “It was heresy to even consider this,” Tobler said. “They really made fun of me in my early years. It wasn’t very pleasant. But I sort of felt time would tell.” She studied scorpions, giraffes, hamsters, cats — 22 species in all. She was convinced that science would eventually confirm that sleep was widespread, and in later studies of sleep, her behavioral criteria would prove critical.

Those criteria were on the minds of Amita Sehgal at the University of Pennsylvania School of Medicine, Paul Shaw (now at Washington University School of Medicine in St. Louis) and their colleagues in the late 1990s. They were part of two independent groups that had begun to look closely at the quiescence of fruit flies. Sleep was still largely the domain of psychologists, Sehgal says, rather than scientists who studied genetics or cell biology. With respect to mechanisms, from a molecular biologist’s perspective, “the sleep field was sleeping,” she said.

However, the neighboring field of circadian clock biology was exploding with activity, following the discovery of genes that regulate the body’s 24-hour clock. If molecular mechanisms behind sleep could be uncovered — if a well-understood model organism like the fruit fly could be used to study them — then there was the potential for a revolution in sleep science as well. Flies, like Tobler’s cockroaches and scorpions, could not be easily hooked up to an EEG machine. But they could be observed minutely, and their responses to deprivation could be recorded.

With Less and Less Brain

In January 2000, Sehgal and her colleagues published their paper asserting that flies were sleeping. That March, Shaw and colleagues published their parallel work confirming the claim. The field was still reluctant to admit that true sleep existed in invertebrates, and that human sleep could be usefully studied using flies, Shaw says. But the flies proved their worth. Today more than 50 labs use flies to study sleep, generating findings that suggest that sleep has a set of core features present across the animal kingdom. And biologists did not stop with flies. “Once we showed that flies slept,” Shaw said, “then it became possible to say that anything slept.”

The sleep that researchers studied in other species was not always similar to the standard human variety. Dolphins and migrating birds can send half their brain to sleep while appearing awake, researchers realized. Elephants spend almost every hour awake, while little brown bats spend almost every hour asleep.

In 2008, David Raizen and his colleagues even reported sleep in Caenorhabditis elegans, the roundworm widely used as a model organism in biology laboratories. They have only 959 body cells (apart from their gonads), with 302 neurons that are mostly gathered in several clusters in the head. Unlike many other creatures, C. elegans does not sleep for a portion of every day of its life. Instead, it sleeps for short bouts during its development. It also sleeps after periods of stress as an adult.

The evidence for sleep in creatures with minimal nervous systems seemed to reach a new high about five years ago with studies of jellyfish. The Cassiopea jellies, about four inches long, spend most of their time upside down, tentacles reaching toward the ocean surface, and pulsating to push seawater through their bodies. When Michael Abrams, now a fellow at the University of California, Berkeley, and two other graduate students at the California Institute of Technology asked if Cassiopea might sleep, they were continuing the line of inquiry that Tobler had followed when she studied cockroaches, investigating whether sleep exists in ever simpler organisms. If jellyfish sleep, that suggests sleep may have evolved more than 1 billion years ago and could be a fundamental function of almost all organisms in the animal kingdom, many of which do not have brains.

Photo of “upside down” Cassiopea jellyfish.

The “upside down” Cassiopea jellyfish does not have a centralized nervous system but it sleeps. The animals never stop moving completely, but at night their rate of pulsations slows, and they show other behaviors associated with sleep.

That’s because, among animals, jellyfish are evolutionarily about as far away as you can get from mammals. Their neighbors in the tree of life include the sponges, which spend their lives attached to rocks in the ocean, and placozoans, tiny clusters of cells first seen by scientists on the walls of seawater aquariums. Unlike other creatures observed sleeping, Cassiopea have no brain, no centralized nervous system. But they can move, and they have periods of rest. It should be possible, the Cal Tech students reasoned, to apply the criteria for behavioral sleep to them.

The first few boxes were relatively easy to check. Although the jellyfish pulsed night and day, Abrams and his collaborators showed that the rate of pulsing slowed in a characteristic way at night, and that animals could be roused from this state with some effort. (There were also indications that the jellyfish favored a particular posture on a platform in the tank during these quieter periods, but Abrams considers that evidence to still be anecdotal.) Testing whether the jellyfish had sleep homeostasis was much harder and required finding ways to gently disturb them without distressing them. In the end, Abrams and his collaborators settled on dropping the platform out from underneath them; when that happened, the Cassiopea would sink and rise again, pulsing at their daytime rate.

The pulsation of a Cassiopea jellyfish can be observed in this series of photos, taken from above. The outer rim of the animal is relaxed at left. It contracts over the next two images, and then relaxes again. The rate of this pulsation helps to indicate sleep in the jellyfish.

Courtesy of Michael Abrams

Later, the telltale signs of homeostatic regulation were there: The more the jellyfish were disturbed, the less the creatures moved the next day. “We weren’t sold on it until we saw the homeostatic regulation,” Abrams said. The team’s results were published in 2017, and Abrams has continued to probe the jellyfish’s genetics and neuroscience since then.

Sleeping in Context

The new revelations about sleep in hydras push the sleep discoveries to a new extreme. The hydra’s body and nervous system are even more rudimentary than Cassiopea’s. Yet as the researchers from Kyushu University in Japan and Ulsan National Institute of Science and Technology in South Korea demonstrated, once a hydra entered a rest state, a pulse of light would rouse it, and it too slept longer after repeated deprivation, among other findings.

Hydra sleep has its peculiarities: Dopamine, which usually makes animals sleep less, caused the hydra to go still. The hydra does not seem to sleep on a 24-hour cycle, instead spending part of every four hours asleep. Something about the hydra’s way of life may have made these traits advantageous, Tobler suggests.

Photograph of Hydra vulgaris eating a copepod.

When it is active, a hydra uses its tentacles to ensnare passing prey. The hydra then pulls its victim into its mouth.

Tom Branch/Science Source

But despite those differences, hydra sleep may overlap with other animals’ sleep at the genomic level. When the researchers looked for gene activity altered by sleep deprivation in hydras, they saw a few familiar ones. “At least some genes conserved in other animals are involved in sleep regulation in hydra,” wrote Taichi Itoh, an assistant professor at Kyushu University and a leader of the new study, in an email to Quanta. That finding suggests that the Cnidaria phylum of animals, which includes hydras and jellyfish, already had some genetic components of sleep regulation before it diverged from the ancestors of other groups of animals. As those animals gradually evolved centralized nervous systems, sleep may have taken on new functions for maintaining them.

What, then, does sleep do in the absence of a brain? Raizen suspects that at least for some animals, sleep has a primarily metabolic function, allowing certain biochemical reactions to take place that can’t happen during waking hours. It may divert the energy that would be used by alertness and movement into other processes, ones that are too costly to take place while the animal is awake. For example, C. elegans seems to use sleep to enable the growth of its body and support the repair of its tissues. In sleep-deprived hydras, the cell divisions that are part of everyday life are paused. Something similar has been seen in the brains of sleep-deprived rats and in fruit flies. Managing the flow of energy may be a central role for sleep.

All this research on very simple sleepers raises questions about the very first organism that slept. This first sleeper, whatever it was, probably vanished more than 1 billion years ago. If it was the common ancestor between hydras and humans, it likely had neurons and something like muscle that enabled it to move — and the absence of that movement was characteristic of its version of sleep, fulfilling its special needs.

“If that animal slept, sleep was for whatever that context was,” Abrams said. Sleep might have helped to maintain the first sleeper’s rudimentary nervous system, but it could just as easily have been for the benefits of its metabolism or digestion. “Before we had a brain, we had a gut,” he said.

Even deeper questions are now being asked. In a 2019 opinion paper, Raizen and his co-authors wondered: If sleep happens in neurons, then what is the minimum number of neurons that can sleep? Can the need for sleep be driven by other kinds of cells, as work implicating liver and muscle cells suggests?

“If you really want to push the envelope, do animals that do not have neurons at all sleep?” Raizen asked.

In fact, there are a few organisms whose behavior might someday reveal the answer. Placozoans, the microscopic multicellular creatures that seem to be among the simplest in the animal kingdom, move and react to their surroundings. They have no neurons and no muscles. Neither do sponges, which are anchored in place but still respond to their environment.

“I’m often asked, ‘Do sponges sleep?’” said Abrams. “That’s a whole new world. There might be ways to test that.”


Tuesday, 24 November 2020

Overtaxed Working Memory Knocks the Brain Out of Sync

Memory 


Overtaxed Working Memory Knocks the Brain Out of Sync

Researchers find that when working memory gets overburdened, dialogue between brain regions breaks down. The discovery provides new support for a broader theory about how the brain operates.

Quanta Magazine

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Humans can hold only four or five items in their conscious awareness, or working memory, at one time. Overloading that capacity causes the neurological juggling act behind working memory to fall apart. Credit: Malina Omut for Quanta Magazine.

In 1956, the renowned cognitive psychologist George Miller published one of the field’s most widely cited papers, “The Magical Number Seven, Plus or Minus Two.” In it, he argued that although the brain can store a whole lifetime of knowledge in its trillions of connections, the number of items that humans can actively hold in their conscious awareness at once is limited, on average, to seven.

Those items might be a series of digits, a handful of objects scattered around a room, words in a list, or overlapping sounds. Whatever they are, Miller wrote, only seven of them can fit in what’s called working memory, where they are available for our focused attention and other cognitive processes. Their retention in working memory is short-lived and bounded: When they’re no longer actively being thought about, they’re stored elsewhere or forgotten.

Since Miller’s time, neuroscientists and psychologists have continued to study working memory and its surprisingly strict limitations. They have found that the limit may really be closer to four or five items than seven. And they have studied the ways in which people work around this constraint: We can remember all the digits of a phone number by “chunking” digits (remembering 1, then 4, as the single item 14, for instance), or develop mnemonic devices for shuffling random digits of pi out of longer-term storage.

But the explanation for why working memory starts to falter at such a seemingly low threshold has been elusive. Scientists can see that any attempt to exceed that limit causes the information to degrade: Neuronal representations get “thinner,” brain rhythms change and memories break down. This seems to occur with an even smaller number of items in patients who have been diagnosed with neurological disorders, such as schizophrenia.

The mechanism causing these failures, however, has remained unknown until recently.

In a paper published in Cerebral Cortex in March 2018, three scientists found that a significant weakening in “feedback” signals between different parts of the brain is responsible for the breakdown. The work not only provides insights into memory function and dysfunction, but also offers further evidence for a burgeoning theory of how the brain processes information.

Synchronized Humming in the Brain

Earl Miller, a neuroscientist at the Picower Institute for Learning and Memory at the Massachusetts Institute of Technology; Dimitris Pinotsis, a research affiliate in his lab; and Timothy Buschman, an assistant professor at Princeton University, wanted to know what sets the capacity limit of working memory so low.

They already knew that a network involving three brain regions — the prefrontal cortex, the frontal eye fields and the lateral intraparietal area — is active in working memory. But they had yet to observe a change in neural activity that corresponded to the steep transition between remembering and not remembering that comes with exceeding the working memory limit.

So they returned to a working memory test that Miller’s lab had performed a few years earlier, in which the researchers showed monkeys a series of screens: first, a set of colored squares, followed briefly by a blank screen, and then the initial screen once more, this time with the color of one square changed. The animals had to detect the difference between the screens. Sometimes the number of squares fell below their working memory capacity, sometimes above. Electrodes placed deep in the monkeys’ brains recorded the timing and frequency of brain waves produced by various populations of neurons as they completed each task.

These waves are essentially the coordinated rhythms of millions of neurons that become active and go quiet simultaneously. When brain areas exhibit matching oscillations, both in time and in frequency, they’re said to be synchronized. “It’s like they’re humming together,” Miller said. “And the neurons that hum together are talking.” He likens it to a traffic system: The brain’s physical connections act like roads and highways, while the patterns of resonance created by these oscillating brain waves “humming” together are the traffic lights that actually direct the flow of traffic. This setup, researchers hypothesize, somehow seems to help “bind” active networks into a firmer representation of an experience.

In their recent work, Miller and his colleagues mined the oscillation data they’d collected from the monkeys for information about how this three-part memory network functions. They built a detailed mechanistic model that incorporated assumptions about the network’s structure and activity, based on previous research: the locations and behaviors (say, excitatory or inhibitory) of specific neural populations, for example, or the frequencies of certain oscillations. The researchers then generated several competing hypotheses for how the different brain areas might be “talking” to one another — including the direction and strength of that dialogue — as the monkeys had to remember more and more items. They compared those computations to their experimental data to determine which of the scenarios was most likely.

MemoryLimits_560.jpg

Credit: Lucy Reading-Ikkanda/Quanta Magazine.

Their modeling confirmed that the three brain regions act like jugglers engaged in a complex game of catch. The prefrontal cortex seems to help construct an internal model of the world, sending so-called “top-down,” or feedback, signals that convey this model to lower-level brain areas. Meanwhile, the superficial frontal eye fields and lateral intraparietal area send raw sensory input to the deeper areas in the prefrontal cortex, in the form of bottom-up or feedforward signals. Differences between the top-down model and the bottom-up sensory information allow the brain to figure out what it’s experiencing, and to tweak its internal models accordingly.

Miller and his colleagues found that when the number of items to be remembered exceeded the capacity of the monkeys’ working memory, the top-down feedback connection from the prefrontal cortex to the other two regions broke down. The feedforward connections, on the other hand, remained just fine.

The weakening of the feedback signals, according to the group’s models, led to a loss of synchrony between the brain areas. Without the prediction-oriented communications from the prefrontal cortex, the working memory network fell out of sync.

Updating the Model

But why is the top-down feedback so vulnerable to an increase in the number of items to be remembered? The researchers’ hypothesis is that the modeled information coming from the prefrontal cortex essentially represents a set of predictions about what the brain will perceive in the world — in this case, the contents of the items being held in working memory. “For example, as you are reading this sentence, you will have expectations about the current word, phrase and sentence,” Karl Friston, a neuroscientist at University College London who was not involved with the study, wrote in an email. “Having a representation or expectation about the current sentence means you have an implicit representation of the past and future.”

Many neuroscientists believe that the brain relies heavily on such “predictive coding” of sensory data to perform its routine cognitive and command functions. But Miller and his colleagues theorize that when the quantity of items placed in working memory gets too large, the number of possible predictions for those items cannot easily be encoded into the feedback signal. As a result, the feedback fails and the overloaded working memory system collapses.

Miller’s lab and others are working to carve out a more important role for the interplay between brain waves in scientists’ model of working memory, which traditionally places most of the emphasis on the firing activity of individual neurons. They’re also currently investigating why the upper bound on working memory hovers around four or five items, and not some other number. Miller thinks the brain is juggling the items being held in working memory one at a time, in alternation. “That means all the information has to fit into one brain wave,” he said. “When you exceed the capacity of that one brain wave, you’ve reached the limit on working memory.”

“The question now is where all this is going to take us,” said Rufin VanRullen, a researcher at the French National Center for Scientific Research who finds the team’s modeling and conclusions “powerful,” pending further experimental confirmation. “We need to actually go inside the brain and find more direct evidence for these connections.”

The potential payoff is high. Cementing a predictive coding model for working memory won’t just enable a better understanding of how the brain works and what might go wrong in neurological diseases. It also has critical implications for what we mean by “intelligence” — and even selfhood, according to Friston. As a start, having a better grasp of what the brain’s feedback connections are doing could lead to big steps in artificial intelligence research, which currently focuses more on feedforward signals and classification algorithms. “But sometimes a system might need to make a decision not about what it sees but based on what it remembers,” Pinotsis said.

Jordana Cepelewicz is a staff writer at Quanta Magazine who covers biology.