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‘Completely new learning mechanism’ drives navigation in fruit flies

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

Chaotic pigeons are helping redefine what we know about learning

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,

Cooperating marmosets extend decision-making model of the brain

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

Are Memories Transferable — or Edible?

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

Bumblebees can solve problems on their own

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.

Reward-learning algorithm hardwired into dopamine circuit

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

The brain’s code seems to be in constant flux. Neuroscientists are baffled

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

What’s Safe to Eat? Birds of a Feather Learn Together

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

New study questions role of persistent gene activity in memory maintenance

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

New Type of Neuroplasticity Rewires the Brain After a Single Experience

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

Head direction cells stably orient mice to outside world

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

Dopamine takes a hit: how neuroscience is rethinking the ‘feel-good’ chemical

David Adam When neuroscientists gather in the Spanish city of Seville in May for the annual Dopamine Society meeting, one discussion could be unusually lively. Session 31 will feature a debate between researchers who fundamentally disagree about the role dopamine has in the brain. Dopamine is one of the most extensively studied neurotransmitters, chemicals that convey signals from cell to cell. It’s the one with the highest profile outside neuroscience: often known as the ‘pleasure chemical’, it’s depicted as the hit of reward that people get from recreational drugs or scrolling through social media. That’s a gross simplification of what dopamine does; on that, researchers agree. But beyond that, where once there was a simple model that explained how dopamine works in the brain, now there are challenges that seek to amend the theory — or even to overturn it. This could have implications not only for basic neuroscience, but also for clinicians trying to explain and treat conditions such as attention deficit hyperactivity disorder (ADHD) and addiction. If the model is wrong or needs modification, then so might some of the assumptions about what drives these disorders and the best way to treat them. The classic idea, known as the reward prediction error (RPE) hypothesis, is that bursts of dopamine in the brain link stimuli to rewards, helping to reinforce associations that fulfil a need for an animal or a person. The model has dominated and guided research in the field for decades, offering a mathematical framework to interpret data from animal experiments, and it does a good job of explaining behaviour. This was a valuable rarity for researchers struggling to overlay simple theories onto the intense complexity of the brain. “Dopamine was the one field of neuroscience where we had a computational model that explained what the signal was and what it was computing,” says Mark Humphries, a neuroscientist at the University of Nottingham, UK. People in the field knew that some of the assumptions involved in the RPE model were simplistic. But as a working understanding of part of the brain, it was seen as a major step forwards. © 2026 Springer Nature Limited

‘Tour de force’ mouse study shows a gut microbe can promote memory loss

By Catherine Offord Scientists have plenty of ideas about why aging impairs memory. Reductions in blood flow in the brain, shrinking brain volume, and malfunctioning neural repair systems have all been blamed. Now, new research in mice points to another possible culprit: microbes in the gut. In a study published today in Nature, scientists show how a bacterium that is particularly common in older animals can drive memory loss. This microbe makes compounds that impair signaling along neurons connecting the gut with the brain, dampening activity in brain regions associated with learning and memory, the team found. “This is a tour de force,” says Haijiang Cai, a neuroscientist at the University of Arizona who studies gut-brain communication and was not involved in the work. “They define the pathway all the way from aging and bacteria … to cognitive function—it’s really impressive.” However, he and others emphasize it remains to be seen whether a similar mechanism exists in humans—and if so, how important it is compared with other drivers of cognitive decline. Research on the so-called gut-brain axis has exploded in recent decades. Multiple studies have identified differences in microbiome composition between healthy people and those with cognitive disorders such as Alzheimer’s disease. This kind of research can’t establish cause and effect, though, and the literature is rife with conflicting results. Some groups have used animal experiments to probe the microbe-memory link. In the new study, Stanford University researchers Christoph Thaiss and Maayan Levy tinkered with the microbiomes of young mice—either by housing them with older animals or feeding them these animals’ poop—and then gave them memory tests. For example, one such test rates animals higher if they spend more time exploring new objects than those they’ve seen before. © 2026 American Association for the Advancement of Science.

‘Tour de force’ mouse study shows a gut microbe can promote memory loss

Hippocampus builds reputation as ‘general-purpose statistical learning machine’

By Natalia Mesa Experience kindles most of our learning throughout life, without any explicit instruction or reward. Thanks to this process, called statistical learning, people unconsciously recognize patterns in their surroundings, and infants soak up language. The hippocampus, it turns out, may be essential for this capability, according to a new preprint, beginning to resolve a long-standing debate. Numerous functional MRI studies have suggested that the structure is involved in statistical learning, but lesion studies have produced mixed results. “This is a tour-de-force study,” says Anna Schapiro, associate professor of psychology at the University of Pennsylvania, who was not involved in the work. “It makes me feel more confident that, yes, the hippocampus is involved in statistical learning, but it’s also necessary for that learning across species.” In the study, people and mice learned to respond—by pressing a key or licking a waterspout, respectively—to a particular sound. As they performed this “cover” task, they also heard an irrelevant four-note sequence at random times, interspersed with the other sound. After repeating this cover task 100 times, both people and rodents showed strong pupil dilation, a sign of surprise, whenever the sequence of notes changed slightly, with more similar sequences evoking a smaller response—indicating that they had passively learned the original musical motif and abstract rules about its structure. Neuronal populations in the hippocampus encoded not only the original and altered tone sequences but also how frequently each occurred. Pharmacologically or optogenetically shutting down hippocampal neurons in the mice prevented them from passively learning the auditory pattern and making generalizations about how often it played, but it didn’t disrupt their performance on the cover task. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30154 - Posted: 03.11.2026

Some Memories Live in the Brain Even If We Can’t Recall Them

By Jake Currie Struggling to remember a forgotten memory is an all-too-common frustration—one that unfortunately becomes more common as we age. We realize that there’s something we can’t recall, but we simply can’t raise it from the depths of our brains. So where did it go? New research published in the Journal of Neuroscience suggests these memories are still lurking in our minds, even though we think they’re long gone. Subscribe to skip ads Featured Video Psychologists from the University of Nottingham led by Benjamin Griffiths strapped participants into a magnetoencephalography machine to measure the magnetic fields surrounding the electrical activity in their brains. Participants were asked to vividly associate a short video clip with a word, and when they were later shown that word, they were asked to recall the video clip while psychologists monitored the magnetic activity of their brains. They found that the brain reactivated memories whether they were consciously recalled or not, meaning the memories were there. When memories were successfully recalled, the reactivated memory signal fluctuated rhythmically in the alpha band. Alpha brain waves, research has shown, are associated with the memorization of visual information, but it was the rhythmicity of the waves that proved key to conscious recall. “What we showed is that even when the brain can reactivate the right memory, it doesn’t guarantee you’ll become aware of it,” Griffiths explained. “Instead, what seems to matter is that the memory rhythmically pulses so that it can be detected above and beyond other neural activity.”

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals
Link ID: 30151 - Posted: 03.07.2026

Dendrites help neuroscientists see the forest for the trees

By Justin O’Hare For decades, two complementary but often siloed approaches have guided neuroscience: cellular neuroscience, which seeks to understand how individual neurons work; and systems neuroscience, which aims to uncover how networks of neurons coordinate to produce thoughts, movements and behaviors. One studies the tree; the other studies the forest. Each approach has produced tremendous advances. For instance, cellular neuroscientists have revealed how ion channels shape the electrical language of the brain, how synapses strengthen or weaken with experience and how gene expression governs neuronal function. Meanwhile, systems neuroscientists have mapped entire circuits, recorded the activity of tens of thousands of neurons during behavior and identified patterns of activity that correlate with memory, decision-making and emotion. But for all these advances, a question lingers: Are we actually any closer to understanding how the brain works? The jaw-dropping datasets produced by systems-level studies are seldom reconciled with biology, and the exquisite detail uncovered by cellular-level studies is rarely extrapolated from circuits to behavior. These disconnects don’t reflect failures of either approach. Rather, they reflect the vast intellectual and material resources that each requires. Nevertheless, the brain is a multiscale organ. It is organized across multiple hierarchical levels operating in concert, not in parallel. To unravel the brain’s deepest complexities, we need to bridge cellular and systems neuroscience. Because of recent technological advances in high-density electrical probes, genetically encoded fluorescent sensors, multiphoton imaging and high-performance computing, we are better suited to do this now than ever before. © 2026 Simons Foundation

Related chapters from BN: Chapter 2: Functional Neuroanatomy: The Cells and Structure of the Nervous System; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 2: Neurophysiology: The Generation, Transmission, and Integration of Neural Signals; Chapter 13: Memory and Learning
Link ID: 30140 - Posted: 02.28.2026

Can you rewire your brain?

Peter Lukacs Popular wisdom holds we can ‘rewire’ our brains: after a stroke, after trauma, after learning a new skill, even with 10 minutes a day on the right app. The phrase is everywhere, offering something most of us want to believe: that when the brain suffers an assault, it can be restored with mechanical precision. But ‘rewiring’ is a risky metaphor. It borrows its confidence from engineering, where a faulty system can be repaired by swapping out the right component; it also smuggles that confidence into biology, where change is slower, messier and often incomplete. The phrase has become a cultural mantra that is easier to comprehend than the scientific term, neuroplasticity – the brain’s ability to change and form new neural connections throughout life. But what does it really mean to ‘rewire’ the brain? Is it a helpful shorthand for describing the remarkable plasticity of our nervous system or has it become a misleading oversimplification that distorts our grasp of science? After all, ‘rewiring your brain’ sounds like more than metaphor. It implies an engineering project: a system whose parts can be removed, replaced and optimised. The promise is both alluring and oddly mechanical. The metaphor actually did come from engineering. To an engineer, rewiring means replacing old and faulty circuits with new ones. As the vocabulary of technology crept into everyday life, it brought with it a new way of thinking about the human mind. Medical roots of the phrase trace back to 1912, when the British surgeon W Deane Butcher compared the body’s neural system to a house’s electrical wiring, describing how nerves connect to muscles much like wires connect appliances to a power source. By the 1920s, the Harvard psychologist Leonard Troland was referring to the visual system as ‘an extremely intricate telegraphic system’, reinforcing the comparison between brain function and electrical networks. © Aeon Media Group Ltd. 2012-2026.

Related chapters from BN: Chapter 17: Learning and Memory; Chapter 7: Life-Span Development of the Brain and Behavior
Related chapters from MM:Chapter 13: Memory and Learning; Chapter 13: Memory and Learning
Link ID: 30108 - Posted: 02.04.2026

How Animals Build a Sense of Direction

By Yasemin Saplakoglu On a remote island in the Indian Ocean, six closely watched bats took to the star-draped skies. As they flew across the seven-acre speck of land, devices implanted in their brains pinged data back to a group of sleepy-eyed neuroscientists monitoring them from below. The researchers were working to understand how these flying mammals, who have brains not unlike our own, develop a sense of direction while navigating a new environment. The research, published in Science, reported that the bats used a network of brain cells (opens a new tab) that informed their sense of direction around the island. Their “internal compass” was tuned by neither the Earth’s magnetic field nor the stars in the sky, but rather by landmarks that informed a mental map of the animal’s environment. These first-ever wild experiments in mammalian mapmaking confirm decades of lab results and support one of two competing theories about how an internal neural compass anchors itself to the environment. “Now we’re understanding a basic principle about how the mammalian brain works” under natural, real-world conditions, said the behavioral neuroscientist Paul Dudchenko (opens a new tab), who studies spatial navigation at the University of Stirling in the United Kingdom and was not involved in the study. “It will be a paper people will be talking about for 50 years.” Follow-up experiments that haven’t yet been published show that other cells critical to navigation encode much more information in the wild than they do in the lab, emphasizing the need to test neurobiological theories in the real world. Neuroscientists believe that a similar internal compass, composed of neurons known as “head direction cells,” might also exist in the human brain — though they haven’t yet been located. If they are someday found, the mechanism could shed light on common sensations such as getting “turned around” and quickly reorienting oneself. It might even explain why some of us are so bad at finding our way. © 2026 Simons Foundation

Related chapters from BN: Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30094 - Posted: 01.24.2026

This tool-using cow defies expectations for bovine braininess

By Erin Garcia de Jesús A deck brush can be a good tool for the right task. Just ask Veronika, the Brown Swiss cow. Veronika uses both ends of a deck brush to scratch various parts of her body, researchers report January 19 in Current Biology. It’s the first reported tool use in a cow, a species that is often “cognitively underestimated,” the researchers say. Cows usually rub against trees, rocks or wooden planks to scratch, but Veronika’s handy tool allows her to reach parts of her body that she couldn’t otherwise, says Antonio Osuna-Mascaró, a cognitive biologist at the Messerli Research Institute of the University of Veterinary Medicine, Vienna. It’s unclear how the cow figured it out, but “somehow Veronika learned to use tools, and she’s doing something that other cows simply can’t.” Veronika, a pet cow that lives in a pasture on a small Austrian farm, picks up the brush by its handle with her tongue and twists her neck to place the brush where she needs it. Setting the brush in front of her in different orientations showed that she uses the hard, bristled end to target most areas, including the tough, thick skin on her back. She also uses the nonbristled end, slowly moving the handle over softer body parts such as her belly button and udder. Veronika uses different parts of a deck brush to reach various parts of her body. She uses the brush end to scratch large areas such as her thigh (top left) and back (top right). She uses the handle to scratch more delicate areas such as her navel flap (bottom left) and anus (bottom right). © Society for Science & the Public 2000–2026.

Related chapters from BN: Chapter 6: Evolution of the Brain and Behavior; Chapter 17: Learning and Memory
Related chapters from MM:Chapter 13: Memory and Learning
Link ID: 30088 - Posted: 01.21.2026

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