By Calli McMurray When bats use echolocation to find an object, they don’t point their sonar beam directly at the target, where the intensity of the signal bouncing back would be the strongest. Instead, they aim slightly off axis, so the returning beam contains sharper signal differences. The research team that observed this in 2010 predicted that the same strategy would apply to scent tracking. That prediction was correct, a paper published in July in Nature shows. When fruit flies catch a whiff of apple cider vinegar, they zigzag along the edge of the odor plume, where the concentration difference is sharpest, rather than traveling through the middle, where a stronger concentration is likely to hold steady. “The edge of the plume is potentially where some of the most information might be stored,” says Marie Suver, assistant professor of biological sciences at Vanderbilt University, who was not involved in the work. “Whereas if you’re in the middle of the plume, you’ll be getting more packets of odor, but it’s not as stark of a concentration gradient as at the edge.” Keeping tabs on a plume is also more complex than researchers previously thought. When flies and other insects first encounter an odor, they surge upwind and cast side to side when they lose the trail—a behavior that seemed to be a simple reflex, says Matthieu Louis, associate professor of molecular, cellular and developmental biology at the University of California, Santa Barbara, who was not involved in the study. “It was supposed to be a memoryless system,” says study investigator Vanessa Ruta, professor and head of the Laboratory of Neurophysiology and Behavior at Rockefeller University. “Basically, all the animal needed to know was the exact sensory experience and information it had at that one moment, and nothing about its prior history would be relevant.” © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 9: Hearing, Balance, Taste, and Smell
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 6: Hearing, Balance, Taste, and Smell
Link ID: 30400 - Posted: 09.09.2026

By Erik Vance Imagine two athletes: a soccer player and a Formula 1 driver. While the soccer player may be blisteringly fast on the field, she still moves at a humanlike speed. She may be quicker than anyone and be able to pivot with incredible dexterity, but it’s always at a pace that the human brain can follow. Now think of the racecar driver, going about 220 miles per hour on the straights and 180 on the corners. No land animal, living or dead, has ever run even half that quickly. It’s simply too fast for any mortal’s brain to keep up with. So, how do they do it? How does a human mind navigate a sport that’s faster than it can follow? “There is a limit, like a physiological limit,” said Otto Lappi, a senior university lecturer at the University of Helsinki in Finland. “But there is less limit to how much cleverness your brain can build in — to know the environment and figure out right now what I need to do not to kill myself.” Scientists have long studied elite athletes as a way to understand the inner workings of the human brain. But Formula 1 drivers offer them a unique window: how the brain adapts to impossible speeds. The first thing to know is that while reflexes are important, they are not what distinguishes a truly elite driver. “This is something that people don’t realize when they think of racing drivers living off their reflexes,” said Dr. Lappi, who studies eye movements of various types of athletes. “Most of their skill is anticipation. The anticipation is how the brain buys itself time.” © 2026 The New York Times Company

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30393 - Posted: 09.05.2026

By Michele Patterson Ford When survivors of trauma recount their experiences, they are often questioned because of how they relay their stories. They may have problems remembering what happened, or their memories may be jumbled or even contradictory. People hearing those stories—attorneys, reporters, healthcare workers or friends—may expect a clearer, linear narrative. Those unaffected by trauma may also be surprised to hear how the person acted in the moment. It’s easy to assume that someone experiencing a traumatic event would have a “fight or flight” response, instinctively fighting off a perpetrator or fleeing the scene—but that might not be the case. Understanding the science of how trauma affects memory and behavior reveals why these responses can actually be expected and are not contradictory. In both my academic and clinical work as a psychologist, I have witnessed the impact trauma has on people’s memories and behaviors, especially when an event in their present life triggers something from their past. Research shows that taking a trauma-informed approach, which prioritizes understanding and curiosity about someone’s experience as opposed to judgment or critical evaluation, increases empathy for survivors’ thoughts, feelings and behaviors. According to the World Health Organization, approximately 70 percent of people worldwide report experiencing a traumatic event in their lifetime. The majority of these people do not develop PTSD, a clinical diagnosis of symptoms, such as having nightmares and flashbacks about the event, avoiding places that are reminders of it, and increased arousal that can make concentration and sleep difficult. Many survivors don’t meet criteria for PTSD but struggle with feeling distressed, depressed or anxious. © 2026 SCIENTIFIC AMERICAN INC.

Related chapters from BN: Chapter 15: Emotions, Aggression, and Stress; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 11: Emotions, Aggression, and Stress; Chapter 13: Memory and Learning
Link ID: 30383 - Posted: 08.22.2026

By Erin Garcia de Jesús In The Sheep Detectives, Mopple has a superpower: The anthropomorphic sheep can’t forget anything. He joins a long line of fictional characters with some version of photographic memory — a strikingly useful skill, or at least a key plot device. Sherlock Holmes uses perfect recall to solve crimes (as long as, in the case of the BBC television series, it’s in his mind palace). And medical student Joy Kwon’s ability comes to the rescue in The Pitt, remembering patient room numbers, symptoms and treatments after the ER loses access to electronic medical records. “A lot of people [believe] that our memory acts like a video recorder, that it exactly captures reality, it holds on to it perfectly,” says Gabrielle Principe, a developmental psychologist at College of Charleston in South Carolina. To debunk that idea, she has a simple task for her students: Draw a penny from memory. None can. Some rare people, mostly children, claim to have what’s called eidetic imagery, or an ability to conjure up mental images of objects even after they’ve been removed from sight. But those images are not perfect and are still temporary. Though exceptional memories exist, everyone forgets. And that is not a flaw. In fact, human brains can’t retain everything. “If we did, we would live in a very cluttered place,” says Simona Ghetti, a developmental psychologist at the University of California, Davis. Our brains wouldn’t know which memories to prioritize, affecting our ability to recall the events or details that matter over unimportant particulars. The ability to retrieve memories of experiences usually starts around age 3, although most early memories are ultimately lost. “There’s always the occasional person who thinks that they remember going through the birth canal,” Ghetti says. But these claims often fall apart upon careful questioning. © Society for Science & the Public 2000–2026.

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30371 - Posted: 08.15.2026

By Natalia Mesa As an animal navigates the world, cells in the hippocampus and entorhinal cortex produce rapid, repeating bursts of activity called theta sweeps: Grid and place cells fire in a specific sequence, first plotting the location the animal has just passed, then where it is currently and lastly what lies ahead. Whether these theta sweeps simply scan the surrounding environment or instead represent the deliberation and planning needed for goal-directed movement is “something that people have been arguing about for 30 years,” says David Redish, professor of neuroscience at the University of Minnesota. That debate may now be over: Theta sweeps serve both functions, depending on the situation, according to three new studies by independent teams. The brain produces systematic sweeps by default to passively sample an environment, but it switches to active, targeted sweeps whenever an animal is pursuing a goal or focused on something specific, the studies show. “It changes our conception of what theta sweeps do,” says Edvard Moser, professor of neuroscience at the Norwegian University of Science and Technology and an investigator on one of the new studies, published today in Science. The other two studies appeared last month in Nature Neuroscience. Theta sweeps occur within individual theta wave cycles, which are around 125-250 milliseconds long. The teams were able to detect the sweeps’ trajectories by recording hundreds of individual neurons at once in 10-millisecond blocks, a time resolution fine enough to see individual theta cycles, Moser says, adding that they are “invisible if you only look at the average.” © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 3: The Chemistry of Behavior: Neurotransmitters and Neuropharmacology
Link ID: 30362 - Posted: 08.08.2026

By Alissa de Chassey A long-standing model of the hippocampus’s role in memory needs to be revised, according to a new preprint. For more than half a century, memory theories treated the CA3 region of the hippocampus as a uniform population of pyramidal neurons that form one broad recurrent, or autoassociative, network; the cells synapse onto each other and also send signals to the CA1 region. The network stores memories as synapses strengthen among coactivated cells, each encoding a different piece of the memory. And because of this architecture, a partial cue can reactivate a full memory, such as when the taste of a madeleine sparks a flood of childhood memories for the narrator of Marcel Proust’s “In Search of Lost Time.” But it turns out that picture may be wrong. The CA3 instead comprises two distinct types of pyramidal neurons arranged in two layers, with different morphology, physiology and connectivity patterns, the preprint suggests. The findings were posted on bioRxiv in July. “These two cell types are very different, and one of them is totally breaking what the textbook would say,” says study investigator Jake Watson, a postdoctoral researcher in Peter Jonas’ lab at the Institute of Science and Technology Austria. A single transcription factor, ST18, distinguishes the two populations, the study reveals: A set of superficial CA3 neurons that express ST18 forms a recurrent network as predicted by the classical model, and a deeper set, which does not express ST18, regulates the superficial one. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30359 - Posted: 08.05.2026

By Natalia Mesa Theoretical models of the brain often treat neurons as single, homogenous units. But dendrites can store information about the past and make predictions about the future independently of the cell body, according to a new study. “In the artificial-intelligence community, dendrites are underappreciated,” says Eilif Muller, associate professor of neurosciences at the University of Montreal, who was not involved in the study. “In this paper, and as we study dendrites more, we’re getting a glimpse into mechanisms that allow us to learn rapidly but stably.” Dendritic activity can dissociate from cell body activity, depending on an animal’s goal, the new work shows. The findings are the first in-vivo evidence of the long-standing theoretical prediction that a neuron’s dendrites play a separate role from cell bodies in neural computations. The study was published in Science earlier this month. “There’s been decades of studies on how dendrites function: Are they passive, or do they play a more active role in cognitive processes?” says study investigator Attila Losonczy, professor of neuroscience at the University of Texas Southwestern Medical Center. Action potentials generated at the soma can backpropagate into the dendrites, making the two compartments’ activity hard to tease apart. Losonczy and his colleagues used ultrafast voltage imaging to record electrical activity in the dendrites of pyramidal place cells in the CA3 region of the hippocampus of mice as the animals moved around in a virtual environment and received a sip of water in certain locations; the place cells fire when a mouse is in a specific location in space. When the reward locations changed, dendrites retained information about the original sites. But when the entire virtual environment changed, dendrites were the first to encode new locations of rewards—the cell body caught up later. © 2026 Simons Foundation

Related chapters from BN: Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 3: The Chemistry of Behavior: Neurotransmitters and Neuropharmacology; Chapter 13: Memory and Learning
Link ID: 30353 - Posted: 08.01.2026

By Emily Anthes One day last summer, a curious white-faced capuchin encountered a strange contraption in the forest. There, in the middle of the Taboga Forest Reserve in Costa Rica, sat a 15-inch touch screen, mounted in a wooden frame. As the monkey, an alpha male named Papi, began investigating — poking the device here, prodding it there — his fingers landed on the screen. Suddenly, a piece of dried banana dropped into a tray below the frame. Before long, Papi learned the basic rules of the device: touch the screen, get a dried banana slice. He also provided proof of concept for CapuchinAI, a new device designed to assess the cognitive abilities of monkeys in the wild. The testing apparatus, which the researchers described in a new paper, used A.I.-powered facial recognition software to detect capuchins in real time and record their responses to a simple learning task. The scientists hope that more sophisticated versions of the device, which they will begin testing in the coming weeks, will shed new light on primate evolution and intelligence, and answer questions that would be impossible to study in a lab, such as how a monkey’s smarts affect its success and survival. “If we really want to understand how primates make decisions, if we want to understand how they’re using these large brains that they evolved, we have to really put it in the context of the world in which they’re navigating,” said Marcela Benítez, a primatologist at Emory University and an author of the new paper, which was published in the American Journal of Primatology on Tuesday. “But that is a lot easier said than done.” Dr. Benítez has been studying the white-faced capuchins at Taboga for years, logging their behavior, recording their vocalizations and measuring their hormone levels. For the new study, she and her colleagues used images of some of these monkeys to train an artificial intelligence model to identify capuchins and distinguish them from the other animals roaming the forest. Then they built a portable testing station equipped with a touch screen, webcam and 3-D-printed food dispenser. © 2026 The New York Times Company

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30349 - Posted: 07.29.2026

By Dylan Loeb McClain Susumu Tonegawa, a Japanese molecular biologist who won the Nobel Prize in 1987 for figuring out how the body can produce sufficient antibodies to combat a multitude of infections, and who later advanced the understanding of how the brain works by discovering how memories are stored, died on July 11 at his home in San Mateo, Calif. He was 86. The Massachusetts Institute of Technology, where Dr. Tonegawa was a professor, announced his death. “Few scientists have reshaped our understanding of biology as profoundly,” Myriam Heiman, the director of M.I.T.’s Picower Institute for Learning and Memory, which Dr. Tonegawa founded in 1994, said in a statement. “His intellectual fearlessness, extraordinary creativity and relentless pursuit of fundamental questions opened entirely new frontiers in both immunology and neuroscience.” For decades, scientists were confounded by the antibodies created in the white blood cells known as B lymphocytes. Those antibodies, which fight disease, are shaped like Y’s, with two long and two short symmetrical chains of proteins built from amino acids, all bound together by bridges of sulfur atoms. Most of the long proteins and some of the short ones are considered constants because they are the same in all antibodies. At the end of each strand are variable amino acids that allow the antibodies to bind to antigens on an array of infections, disabling them. A common analogy is that the constant amino acids are like the shaft of a key, and the variable ones are the notches that turn the lock. As with a key, each combination of notches is unique. Even so, scientists were puzzled by how the antibodies could create enough combinations to fight millions of infections. © 2026 The New York Times Company

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30333 - Posted: 07.22.2026

By Natalia Mesa To accurately navigate the world, an animal must learn, remember and continually update how its body position relates to what it sees in the world around it. New findings reveal the circuit mechanisms responsible for this process in fruit flies—and upend a widely held assumption that this kind of learning relies on dopamine. The research “solves this long-standing problem of how you learn about landmarks in the world,” says Lisa Giocomo, professor of neurobiology at Stanford University, who was not involved in the study. “Over the last decade, some of the biggest insights into how the brain generates algorithms for navigational systems have come from Drosophila,” she says. “It’s been astonishing to see what’s been possible with that system.” When a neuron in a fly’s internal compass activates at the same time as a cell responding to a visual landmark, a third type of cell called an EL neuron releases the neuromodulator octopamine onto the visual inputs, according to the work, posted as a preprint in December 2025 and presented at the Jane Coffin Childs Symposium in May 2026. Octopamine acts as a signal that modifies the connection between the compass and visual cells, anchoring the fly’s sense of direction to visual cues. To their knowledge, the synaptic and circuit mechanisms the fly uses to update its internal compass work unlike any yet described, the study investigators say. “It’s a completely new learning mechanism, basically,” says Stanley Heinze, senior lecturer of sensory biology at Lund University, who was not involved in the study. Fruit flies, like other animals, have an internal compass made up of head direction cells that selectively activate based on the direction the fly faces. The fly’s internal representation of the world drifts without visual input but quickly reorients when familiar landmarks reappear. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30313 - Posted: 07.08.2026

By K. R. Callaway Strutting and fluttering around cities, pigeons have adapted to an ever-shifting environment. But their environment isn’t the only thing that’s constantly changing. New research suggests the birds themselves avoid stability in their decision-making, instead choosing to live “at the edge of chaos.” As model species for learning and behavior, these birds are helping researchers test a century-old law about how humans and other creatures learn. When learning something new, people and animals alike tend to repeat behaviors that are rewarded. First proposed by Edward Thorndike in 1898, this principle is so well established in psychology that it's become known as the law of effect. But the law implies that beyond making a behavior more frequent, rewards also make it more consistent: reducing variability in the specific way behaviors are performed over time. Although scientists have repeatedly tested whether rewards increase the frequency of behaviors, their effect on consistency is less well studied. University of Iowa experimental psychologist Edward A. Wasserman and his colleagues decided to put it to the test in pigeons—a species that has been integral to the study of learning at the university’s Comparative Cognition Laboratory for more than 50 years. And the study’s results, published in the Journal of Experimental Psychology: Animal Learning and Cognition, suggest these birds experience variability as the spice of life. To see how rewarded behaviors vary, the researchers gave pigeons a series of five colorful buttons to peck. They could peck any buttons in any order, but as long as they pecked five times, a treat would appear. Based on previous theories of learning, the scientists expected the pigeons might eventually slip into a routine—perhaps choosing to repeat patterns they know work or simply pecking the button nearest to them five times. Instead they continued pecking in a variety of patterns. © 2026 SCIENTIFIC AMERICAN,

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30304 - Posted: 07.01.2026

By Calli McMurray Kanga the marmoset places her hand on the lever and looks at Dodson, a fellow marmoset working with her on a task. As it becomes apparent that Dodson is ready to pull his own lever, neurons in Kanga’s dorsomedial prefrontal cortex ramp up their firing. The activity reaches its peak as Kanga decides to pull the lever, in sync with her partner. As a reward for their coordinated effort, both marmosets earn a sip of liquid marshmallow fluff. This type of neuronal computation underlies the “evidence accumulation model,” a major theory of how perceptual decisions are made: The brain gathers evidence and executes a decision once the evidence reaches a certain threshold. The marmoset study, which was published last month in Neuron, demonstrates that the model also applies to social decisions. This result wasn’t a given; making a social decision relies on the changing behavior of another animal, and the actions of the decider can influence what the other animal does, says study investigator Monika Jadi, associate professor of psychiatry and neuroscience at Yale University. “It’s a very recurrent system,” she says. Support for the evidence accumulation model has come largely from highly controlled experiments; the fact that the same activity pattern appears in a social and less constrained task “implies that this is a generalizable computation,” says Timothy Hanks, associate professor of neurology at the University of California, Davis, who was not involved in the work. Social, perceptual, foraging and other decisions are “categories we’ve created,” but there may not be anything “acutely different” about them, says Cory Miller, professor of psychology at the University of California, San Diego, who was not involved in the study. “I love this line of work; I think it’s super powerful.” © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 19: Language and Lateralization
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 15: Language and Lateralization
Link ID: 30297 - Posted: 06.27.2026

By Claire L. Evans It was the dead of winter in Boston. The surface of the Charles River was frozen solid. But Zachary Kelso (opens a new tab) braved the biting cold to finally put to rest a mystery that has haunted neuroscience labs for over half a century. To do that, Kelso, a research assistant in the Harvard lab of the neuroscientist Sam Gershman (opens a new tab), needed some worms. Specifically, planarians: arrow-headed flatworms, which are among the simplest creatures to possess a brain and a nervous system with bilateral symmetry like ours. Normally, labs order these widely used model organisms from biological supply companies. But the mail-order worms weren’t up to snuff. So Gershman had dispatched Kelso to the Charles’ icy banks to catch some wild ones. “I thought, ‘I’m going to look crazy because I’m using a hammer to beat through the ice,’” Kelso recalled. “So I wore the more business end of business casual.” In philosophy, “qualia” refers to the subjective qualities of our experience: what it’s like for Alice to see blue or for Bob to feel delighted. Qualia are “the ways things seem to us,” as the late philosopher Daniel Dennett put it. In these essays, our columnists follow their curiosity, and explore important but not necessarily answerable scientific questions. It wouldn’t be the last time Kelso found himself in this situation. The Charles River planarians, it turned out, didn’t cut it either. Neither did the worms he sourced while stream-hopping around Eugene, Oregon, in March 2025. Nor did the ones he fished from Michigan lakes that June — this time in thigh-high waders — while picnicking families gawked from shore. Kelso diligently turned over rocks, angled with bits of meat tied to a string, and even followed maps from a vintage guidebook called The Fresh-Water Triclads of Michigan (opens a new tab). But his adventure was fruitless. Sure, he caught plenty of planarians. But back in Gershman’s lab, none of them would do what they were supposed to do. (C) Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30272 - Posted: 06.06.2026

By Erin Garcia de Jesús Buff-tailed bumblebees can figure out on their own how to use a ball as a ladder to nab sugar from an out-of-reach fake flower, researchers report in the June 4 Science. The insects worked out the trick without specific training for the solution, suggesting a remarkable capacity for solving problems. Bumblebees are brainy, with studies showing they may have emotions and can teach one another to score goals in a six-legged version of soccer. The new finding adds yet another skill to their repertoire. “Spontaneous problem-solving is something that has never been shown in any invertebrate before,” says Olli Loukola, a behavioral ecologist at the University of Oulu in Finland. Vertebrates including chimpanzees and parrots can problem solve on their own, although researchers typically focus on captive animals with plenty of experience working out puzzles. “Our study is the first one where we can be 100 percent sure that these individuals don’t have any prior experience about any problem-solving tasks,” Loukola says. Loukola and colleagues first taught bees two necessary associations: Balls are moveable objects and a blue ring — representing a flower — means food. The team then let the bees loose in plexiglass arenas too small for them to fly to reach a blue ring printed on the ceiling. © Society for Science & the Public 2000–2026.

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 6: Evolution of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30271 - Posted: 06.06.2026

By Natalia Mesa Dopamine neurons register surprise: Their activity surges when an experience exceeds expectations and falls silent with disappointment. These prediction errors help brains and artificial-intelligence systems learn from experience by updating future expectations, according to a long-standing model. But because dopamine neurons receive input from several sources, the exact circuit mechanisms that compute the difference have remained mysterious, says Naoshige Uchida, professor of molecular and cellular biology at Harvard University. It turns out that a circuit of just two types of neurons is central to this computation. Dopamine neurons in the ventral tegmental area calculate the error based on input originating from D1 medium spiny neurons in the striatum, according to unpublished mouse data Uchida and his team presented at this year’s Computational and Systems Neuroscience (COSYNE) annual meeting and reported in a preprint posted on bioRxiv in October 2025. This result suggests that “reward learning doesn’t necessarily involve higher-order computation,” says Kauê Costa, assistant professor of psychology at the University of Alabama at Birmingham, who was not involved in the work. “The canonical view is that these types of computations would involve higher-order areas.” But it also bolsters the reward prediction error model, which has come under scrutiny in recent years, says Nathaniel Daw, professor of computational and theoretical neuroscience at Princeton University, who was not involved in the study. “It’s amazing” how much explanatory power the model has had in predicting neuronal responses, he adds. “It’s been a long road to get here. It’s a really beautiful study.” © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 18: Attention and Higher Cognition
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 14: Attention and Higher Cognition
Link ID: 30270 - Posted: 06.06.2026

Diana Kwon It is a dogma in neuroscience that certain brain cells respond in the same way to the same thing. Specific neurons always fire, for example, when we see particular shapes and colours; other neurons activate to swing an arm or wiggle a nose. The brain needs this stability, the theory goes, to respond to the outside world in a consistent way. So, when neuroscientist Laura Driscoll began her doctoral research at Harvard University in Cambridge, Massachusetts in 2012, her first task was to establish this baseline by tracking the activity of individual mouse neurons over time. To Driscoll’s surprise, the baseline kept moving. Over the course of several days, many of the cells’ responses had shifted noticeably. Neurons that had fired when a mouse was in a specific location on day one were barely responding in the same spot after a few weeks. “It absolutely defied all of our expectations,” recalls Driscoll, who is now at the Allen Institute in Seattle, Washington. “This was so surprising that my whole project changed.” In 2017, she and her colleagues reported findings from that project that flew in the face of neuroscience dogma. Over a single day, neurons in the parietal cortex, a hub for processing sensory information, fired predictably in response to specific things, such as the position of the mouse in a virtual maze. But over the course of a few weeks, even though the task of navigating the maze remained the same, these activity patterns underwent major reorganization1. Some of the neurons stopped firing in response to stimuli that had previously activated them; others did the reverse. In groups of cells, however, patterns of neuronal activity remained more consistent over time. The results suggested that individual neurons might not have fixed roles, and that the response of single cells might be less important than the activity of whole populations. © 2026 Springer Nature Limited

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 3: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 3: The Chemistry of Behavior: Neurotransmitters and Neuropharmacology
Link ID: 30251 - Posted: 05.20.2026

By Kate Golembiewski By watching their peers, dolphins learn to capture fish in empty conch shells, then ferry the shells up to the water’s surface in order to eat. Octopuses can master experimental tasks by watching their tankmates in the laboratory. Crows follow the cues of others in their flock to attack specific humans who have harassed fellow crows in the past. Scientists call it “social learning,” and it essentially means monkey see, monkey do, an adage that turns out to apply to many animals beyond just primates. Now, a study of Australia’s sulfur-crested cockatoos shows that the birds employ social learning to understand whether unfamiliar foods are safe to eat. In more forested areas of the cockatoos’ native range in Australia, New Guinea, and Indonesia, these mohawked parrots eat plant roots, seeds, fruits and insect larvae. But the birds have learned to thrive in urban environments. “They’re everywhere in Sydney,” said Julia Penndorf, a behavioral ecologist and lead author of the study in PLOS Biology, who encountered the birds as a postdoctoral researcher at the Australian National University in Canberra. In urban areas, the birds have expanded their diets to include nonnative plants and nuts, including almonds and sunflower seeds people offer to them, and they can be seen prying the lids off garbage bins in order to forage. “The big issue with urban birds is, they kind of eat everything,” Dr. Penndorf, who now works at the University of Exeter, said. This expanded diet is high-risk, high-reward: the birds have more options for food, but there’s always a chance that strange new snacks might be poisonous. © 2026 The New York Times Company

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 13: Homeostasis: Active Regulation of the Internal Environment
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 9: Homeostasis: Active Regulation of the Internal Environment
Link ID: 30229 - Posted: 05.02.2026

By Siddhant Pusdekar Transcriptional changes are essential for converting new experiences into memories but may not be required to make memories last, a new study suggests. The findings, published in eNeuro in March, conflict with a model proposing that positive feedback loops of transcription can help maintain long-term memories, says study investigator Irina Calin-Jageman, professor of biological sciences at Dominican University. But they open up a set of hypotheses about how transcription maintains long-term memories and indicate that the handful of genes whose regulation persists for up to two weeks could be “really key,” she adds. The results, obtained in the sea slug Aplysia californica, are “one small step on our way to understanding this very important question of: What is the role of transcription in forming long-term memories?” says Wayne Sossin, distinguished James McGill professor of neurology and neurosurgery at McGill University, who is listed as a reviewer for the paper. Disproving models doesn’t “get the attention it deserves, I think, from the scientific community,” he says, but science is built on overturning theory. Irina Calin-Jageman and her colleagues focused on the transcriptional traces of a partially faded memory in the sea slug. When the animal feels threatened, it retracts a breathing apparatus on its back called a siphon. After traumatic experiences—such as induced shocks—the slug retracts its siphon for longer than usual, previous work showed. Also, sensory neurons in the pleural ganglia change their gene expression patterns and remain more excitable for up to 24 hours, and synaptic changes can last for several days to weeks, depending on the training. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30223 - Posted: 04.29.2026

By Yasemin Saplakoglu Every experience we have changes our brain, the way a ceramicist reshapes a slab of clay. Every corner we turn, every conversation we have, every shudder we feel causes cascading effects: Chemicals are released, electricity surges, the connections between brain cells tighten, and our mental models update. The brain is “incredibly plastic, and it stays that way throughout the lifespan of a human,” said Christine Grienberger (opens a new tab), a neuroscientist at Brandeis University. This plasticity, the quality of being easily reshaped, makes the brain really good at learning — a quintessential process that allows us to remember the plotline of a novel, navigate a new city, pick up a new language, and avoid touching a hot stove. But neuroscientists are still uncovering fundamental rules that describe how neuroplasticity reshapes brain connections. Recently, neuroscientists described a new form of neuroplasticity that might be helping the brain learn across a timescale of several seconds — long enough to capture the behavioral process of learning from a single experience. In two recent reviews, published in The Journal of Neuroscience (opens a new tab) and Nature Neuroscience (opens a new tab), they describe “behavioral timescale synaptic plasticity,” or BTSP. This type of learning in the hippocampus, the brain’s memory hub, is caused by an electrical change that affects multiple neurons at once and unfolds across several seconds. Researchers suspect that it may help the brain learn in a single attempt. “It’s pretty clear that [BTSP is] a strong, powerful mechanism that can lead to immediate memory formation,” said Daniel Dombeck, a neuroscientist at Northwestern University who was not involved with the theory’s development. “It’s something that has been missing in the field for a long time.” © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30219 - Posted: 04.26.2026

By Angie Voyles Askham The idea that some neural representations can “drift,” or change over time, even in the seeming absence of learning, is broadly accepted. But characterizing the phenomenon across the brain has proved challenging. “The interesting part is what exactly seems to be stable and what exactly seems to be drifting. That’s not an easy question,” says Tobias Rose, a group leader at the University of Bonn Medical Center, who presented findings on drift in the mouse primary visual cortex earlier this month at the Computational and Systems Neuroscience (COSYNE) annual meeting. Other new research adds nuance to the discussion: Neurons that code for head direction in the mouse post-subiculum show little drift, retaining their tuning for multiple weeks, according to a study published last month in Nature. And they differ from hippocampal place cells, which are also part of the spatial navigation system but have highly variable responses, as reported in previous research. The new findings raise questions about how stable and flexible representations interact in the brain, given that signals from the post-subiculum ultimately feed into the hippocampus, says Rose, who was not involved in the work. “It’s a rather important study,” he says. The relative stability of head direction cell tuning does not invalidate previous reports of drift elsewhere in the brain, says Adrien Peyrache, associate professor at the Montreal Neurological Institute, who led the head direction study. Instead, it may be that these invariant responses act as a “rigid backbone” onto which more flexible sensory and cognitive responses can be mapped, he says. “I find it reassuring.” Still, the low drift reported in the new work may be partially due to the study’s methods, which eliminated cells that lost their response from one day to the next, says Timothy O’Leary, professor of information engineering and neuroscience at the University of Cambridge, who was not involved in the work. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30181 - Posted: 03.28.2026