You’re on a beginner ski run and the instructor asks everyone to make a “pizza” with their skis: tips together, heels apart. In theory, it’s simple. In practice, it’s awkward. One ski drifts out, the wedge collapses, and suddenly you’re moving faster than you meant to. You stop, try again, and push the tips inward a little more. Now the skis scrape and shake because you’re forcing them instead of guiding them. A few runs later, something’s different. The wedge holds sooner. Your speed drops when you expect it to. It’s the same hill, but the movement doesn’t feel as unfamiliar anymore.

What’s changing isn’t the snow. It’s how the brain is adapting to the task. Neuroplasticity describes the brain’s ability to reorganize its connections through experience. Billions of neurons are firing in patterns at any moment, and with repetition some routes can be strengthened while others may gradually fade, a bit like paths in a park that become clearer the more often they’re walked. Each attempt on the slope generates feedback. The series of movements either works or doesn’t. That signal is carried into the next attempt, where balance, timing, coordination, and pressure across the skis are synchronized a little more effectively. Early on, nearly everything competes for attention. With enough repetition, parts of the sequence may begin to run with less effort, and the corrections often arrive earlier.

Even off the ski hill, the brain deals with a constant stream of input. Conversations, screens, routines, stress, sleep, and whatever gets repeated day after day. Activity settles into circuits that are used together often, and those circuits may strengthen, while less-used links may be trimmed over time. Neuroplasticity is the umbrella term for those experience-driven updates, describing how the brain’s wiring adjusts as life keeps happening.

That same process supports adaptability in different contexts. With aging, plasticity may show up as gradual refinement, keeping frequently used skills efficient and available. After injury, it can involve reorganization, with surviving regions being recruited in new ways as movement, speech, or daily tasks are relearned. The pace and degree of change may vary, but it’s still the same basic principle: repeated experience can reshape how neural circuits are built and used.

Neuron: Often described as the basic unit of the nervous system, a neuron is a specialized cell that receives sensory input from the external world, sends motor commands to muscles, relays electrical signals throughout the brain and body, and helps coordinate communication between different parts of the brain.

Synapse: The tiny gap between neurons, the synapse allows signals to pass from one cell to the next and creates the connections through which neural communication occurs.

Long-term potentiation (LTP): A long-lasting increase in synaptic efficiency after repeated, patterned activity. In LTP, a synapse becomes better at transmitting signals so the same presynaptic activity is more likely to trigger a response in the next neuron. 

White matter: Brain tissue made of long nerve fibers that connect regions within a hemisphere, across hemispheres, and between the brain and spinal cord. It supports fast, coordinated communication between areas.

Neurogenesis: The process of generating new neurons in the brain. 

Transcranial Magnetic Stimulation (TMS): Used as a non-invasive treatment, TMS delivers repeated magnetic pulses through a coil placed on the scalp. These pulses can either increase or decrease activity in targeted brain regions.

Spike-timing-dependent plasticity (STDP): Depending on the precise timing of neuronal firing, the connection between two neurons may either strengthen or weaken, making timing itself central to changes in synaptic strength. Even tiny differences in timing can shape how strongly neurons connect.

In 1690, philosopher John Locke claimed that all knowledge develops exclusively through experience and sensory input, famously likening the mind to “white paper, void of all characters.”² That premise became testable once anatomists began asking whether extended practice could leave a physical trace. In 1793, Michele Vincenzo Malacarne, an Italian pioneer in neuroanatomy, compared animals raised in more enriched environments with matched animals raised in less stimulating ones, then examined their brains after death.³ Later summaries report that the animals with more experience and training showed a more developed cerebellum than their counterparts. Since the cerebellum supports motor learning and cognitive functioning, it’s a plausible place for a richer environment to leave an anatomical trace. Even so, the finding didn’t become a standard reference point at the time. The tools for linking experience to specific brain mechanisms were limited, so researchers could report only a structural difference without a clear account of how it was produced.

By 1890, William James introduced “plasticity” as a way to describe experience-dependent change in the nervous system. In The Principles of Psychology, he linked habit formation to lasting modification of nervous pathways, implying that repetition can shape the biological substrate of behavior as actions become easier and more automatic with practice.⁴ The term gave later researchers a shared language for studying how experience can alter neural organization.

A biological account of neuroplasticity became more believable once the nervous system was understood as a set of individual units, called neurons, rather than a single continuous network. Using staining methods and microscopy, Spanish neuroscientist and Nobel laureate Santiago Ramón y Cajal argued that the brain is made of distinct neurons that communicate through contact points, countering the then dominant view that nervous tissue formed one continuous network.⁵ 

As the idea of discrete cells and connections—the neuron doctrine— became the widely-accepted framework, it was easier to propose that learning could involve changes in how those connections are arranged or strengthened. In Cajal’s public lectures, he also described a “cerebral gymnastics” concept, suggesting that practice could increase neuronal connections, a view that aligns with modern descriptions of experience-dependent neuroplasticity.⁶

After the neuron doctrine took hold, plasticity could be framed as changes in neuronal connections, which made learning a logical next focus in neuroscientific research. Donald Hebb offered such a rule in 1949, providing a conceptual blueprint. In The Organization of Behavior, he proposed that when activity in one neuron repeatedly contributes to activity in another, the connection between them can be strengthened, offering a plausible mechanism for associative learning.⁷ The saying “neurons that fire together, wire together” is often used to summarize the idea, even though Hebb didn’t write that exact line himself. 

Physiology then supplied a candidate mechanism for neuroplasticity at the synapse. In 1973, Timothy Bliss and Terje Lømo stimulated inputs to the hippocampus, a memory-related region, and recorded how strongly neurons responded before and after repeated bursts of stimulation.⁸ After those bursts, the same input produced a larger response that persisted, indicated the connection had been strengthened rather than temporarily excited. Called long-term potentiation (LTP), this effect is a direct example of neuroplasticity because it shows a lasting change in synaptic strength driven by patterned activity. In other words, repeated activation didn’t just make neurons fire once, it made the pathway easier to activate again later.

Once synaptic plasticity had been shown to change connections with experience, a bigger question followed. Could the adult brain also generate new neurons, rather than only changing the strength of existing ones? That process is called neurogenesis, the generation of new neurons. In 1998, Peter Eriksson and colleagues reported evidence consistent with neurogenesis in the adult human hippocampus using postmortem brain tissue.⁹ As part of their medical care, patients received a DNA-labeling compound that marks newly formed cells. In their subsequent analysis, the team reported new, labelled neurons in the dentate gyrus, a hippocampal subregion involved in learning and memory. Adult neurogenesis in humans remains debated today, but Eriksson's study became a landmark because it treated “new neurons in adult humans” as a testable claim.

More recently, research has expanded from observing plastic changes to deliberately modulating them, including with non-invasive stimulation methods. Techniques such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) aim to alter cortical excitability, meaning they can change how readily targeted neural populations fire, at least temporarily.¹⁰ Since plasticity depends on patterns of activity, changing excitability may influence how readily connections strengthen or reorganize, especially when stimulation is paired with practice or therapy. 

Repetitive TMS has been cleared by the U.S. Food and Drug Administration (FDA) for medication-resistant depression, with early clearance reported in 2008, and clinical use has expanded since then.¹¹ From there, neuromodulation has increasingly been explored in stroke rehabilitation, the treatment of chronic pain, and other mental health and substance use disorders.^12-14

Across these milestones, the brain comes into view as a flexible system that’s continually being remodeled by experience. Researchers can now point to several plausible mechanisms, from synaptic strengthening to large-scale circuit reorganization, but much remains to be discovered about how these processes interact.¹⁵

Michele Vincenzo Malacarne 

In the late 1700s, Italian anatomist Michele Vincenzo Malacarne ran early animal comparisons to see whether enriched environments could lead to changes in brain structure during development.³ He was also among the first to systematically map the brain’s cerebellum, a region at the back of the brain that helps fine-tune movement, balance, and timing.

William James

Often called the “father of American psychology,” William James helped establish psychology as a formal subject in the United States. In The Principles of Psychology (1890), he used the term “plasticity” to describe changes in nervous pathways associated with habit formation, giving researchers a shared language to describe how repetition could shape the nervous system.⁴

Santiago Ramón y Cajal

In the late 1800s and early 1900s, Spanish neuroscientist Santiago Ramón y Cajal used microscopic staining techniques to show that the brain is made of individual cells called neurons.⁶ That claim wasn’t widely accepted at first because many scientists believed the nervous system was one continuous network, but Cajal’s neuron doctrine changed how learning could be explained, as changes in the connections between cells.

Donald Hebb

Canadian psychologist Donald Hebb, often described as a foundational figure in neuropsychology, aimed to explain how neural activity could give rise to psychological processes like learning. In The Organization of Behavior (1949), he proposed that when one neuron repeatedly contributes to activating another, their connection may strengthen.⁷ The idea is often summarized by the phrase “neurons that fire together wire together”.

Timothy Bliss and Terje Lømo

In 1973, British neuroscientist Timothy Bliss and Norwegian neuroscientist Terje Lømo reported one of the first findings on LTP, which refers to a lasting increase in how strongly one neuron signals to another after repeated stimulation.⁸ Their results provided future researchers a concrete way to study learning as a measurable change at the synapses between neurons.

Peter Eriksson

In the late 1990s, Swedish neuroscientist Peter Eriksson reported findings consistent with adult hippocampal neurogenesis in humans.⁹ His study became widely cited because it challenged the long-held belief that the adult human brain could no longer generate new neurons.

The discovery that neural circuits can change in response to experience reshaped multiple scientific and clinical fields. It’s been applied to rehabilitation after stroke, to sensitive periods like adolescence, and even to computing systems that borrow learning rules from synapses.

Stroke rehabilitation and recovery after brain injury

Some of the clearest evidence for neuroplasticity comes from stroke and motor recovery research. Stokes often damage brain regions involved in movement control, leaving lasting weakness or poor coordination on one side of the body. Over the past few decades, studies in animals and humans have shown that activity patterns in surviving brain regions can change during rehabilitation, especially when impaired movements are practiced repeatedly.^16,17 In other words, the circuits that remain undamaged may start working differently as the body relearns how to move.

Animal studies helped clarify how this reorganization might occur. In squirrel monkeys, researchers created small lesions in the motor cortex, the region involved in voluntary movement of the hand and arm.¹⁷ When the animals received no retraining, the nearby hand area in the undamaged cortex became smaller over time. When the monkeys were trained repeatedly on reaching tasks, that loss was prevented. In some cases, the hand representation expanded into neighboring areas that had previously been associated with adjacent limbs.¹⁷ Practice wasn’t only improving performance; it was linked to measurable changes in the brain’s motor map.

Human stroke rehabilitation has followed similar principles. One well-known approach is constraint-induced movement therapy (CIMT), which was developed to counter “learned non-use.”¹⁸ After a stroke, people often rely heavily on the unaffected arm, and the weaker arm can end up getting used less and less even when some movement is still possible. CIMT tries to break that pattern by limiting use of the unaffected limb while carrying out intensive practice with the affected arm. Imaging studies of CIMT have reported changes in the primary sensorimotor cortex, the strip of tissue that helps control movement and processes sensory feedback from the body.¹⁹ After therapy, activity there may increase or become more balanced, suggesting that surviving circuits are being recruited differently as practice continues.

Sensitive periods and substance use risk

Plasticity has windows. During certain developmental periods, brain circuits may be more responsive to experience, so the same input can leave a larger and longer lasting trace than it would outside of those time windows.²⁰ Researchers often call these critical or sensitive periods: times when a system’s wiring is easier to tune. Supportive input can speed up learning, while repeated stressors or heavy exposure can also be absorbed more deeply, altering developmental trajectory in ways that persist.

Adolescence is often described as a sensitive developmental period, one in which the brain stays especially open to being shaped by experience. During this phase, neuroplasticity is thought to refine brain circuits involved in emotion processing, motivation, stress, and self-control. Connections that get used frequently typically become strengthened, while less-used connections may be pruned, making overall communication between different brain regions more efficient. Since the system is still being built, environmental stressors, such as substance exposure during the teen years, have been studied as factors that could alter typical development.²¹

In research examining alcohol use during the teenage years, longitudinal neuroimaging work has found that youth who engage in heavy drinking youth show accelerated cortical thinning in frontal and temporal regions and reduced development of the white matter— the brain’s “wiring” that helps different regions communicate—compared with non-drinking peers.²² These findings suggest alcohol may negatively influence circuits involved in executive control and decision-making.

Animal models have also helped clarify what that influence might involve at the circuit level. In a study of binge drinking modeled in adolescent rats, intermittent alcohol exposure was followed by lasting differences during withdrawal, including reduced sensitivity to rewards and increased anxiety-like behavior in social or novel situations.²³ Those behavioral changes were accompanied by neuroadaptations in the nucleus accumbens, a key hub in the brain’s reward system. Two chemical messenger systems were implicated: dopamine, which helps the brain tag experiences as motivating or worth pursuing, and glutamate, which helps carry learning signals and strengthen connections between neurons.

When heavy substance exposure or repeated stressors occur during sensitive developmental periods, neuroplastic changes may alter reward and stress circuitry, which may later appear as differences in motivation, anxiety, and everyday decision-making.

Neuromorphic computing

Neuromorphic computing is one place where neuroplasticity has influenced modern AI design. The aim isn’t to build a brain in silicon. Instead, the goal is to borrow learning principles from biology and apply them where conventional AI can be inefficient, especially when conditions keep changing and power is limited. Many systems are trained in a single intensive phase and then deployed as mostly fixed models, so there’s limited room for learning once they’re in the world. Hardware that can keep updating in small steps, without constant retraining elsewhere, can start to look more practical.²⁴

That’s where event-based signaling comes in. Neuromorphic systems often use “spikes,” brief on–off signals that carry timing information, like a quick tap that says something changed. When nothing changes, fewer spikes are sent and the system can stay relatively idle.24 When the environment changes, spike activity increases and computation scales up. It’s closer to a motion-sensor light than an always-on light, so energy use can drop when inputs are sparse. This approach has been explored for real-time sensing and robotics, where signals arrive in bursts and fast responses are needed.²⁵

The most direct plasticity link sits in the learning rule. A common one is spike-timing-dependent plasticity (STDP), which changes connection strength based on timing.²⁵ If one unit repeatedly fires just before another, the connection between them may strengthen. If the order reverses, it may weaken. That echoes a core feature of neuroplasticity, in which synapses, the brain’s connection points, can change strength with repeated activity. Spiking neural networks build on this logic by using spike-like signals and connection updates to learn patterns over time.

Newer chips have been built to scale this approach, and Intel Loihi is a clear example.²⁶ It supports spiking neural networks with programmable learning rules, so connections can be updated while the chip’s running, rather than being fixed after training. The design goal is efficiency, since the brain can run massive parallel computations on surprisingly low power by relying on event-based signals and gradual connection updates. Neuromorphic systems are trying to move closer to that operating style, where learning can keep happening without constant, power-hungry computation.

Neuroplasticity is widely accepted, but several high-profile claims about how it works are still debated. For example: Do adults actually grow new neurons? Do brain-training games improve anything beyond the game itself? And do psychedelics drive lasting rewiring or mostly short-lived changes in brain activity?

Does adult neurogenesis actually occur in humans?

Adult neurogenesis is the idea that new neurons might still be generated in adulthood, most often in the hippocampus, a memory-related region.⁹ The claim is controversial for a simple reason; neuroplasticity is well-supported, but “plasticity” doesn’t always mean new neurons are being made. Some human studies have reported signals consistent with immature neurons in adult hippocampal tissue, while other groups have found that those signals drop sharply after childhood and are rare or absent in adults, so there isn’t a single story that everyone agrees on.^9,27

A lot of the debate comes down to detection and interpretation. One of the leading research teams in this area examined human hippocampal tissue across the lifespan and used several cross-checks rather than relying on a single marker.²⁷ They assessed multiple proteins linked to young neurons, inspected cell shape under high-resolution microscopes, compared broader gene-expression patterns, and checked markers of dividing stem cells. They reported that they couldn’t find convincing evidence of new neurons in the adult hippocampus and concluded that if neurogenesis continues, it’s probably extremely rare.

Marker specificity is a sticking point too. Some proteins often used as “young neuron” markers can also appear in glial cells—the brain’s support cells that help keep neurons functioning and that continue to renew throughout life. If a marker lights up glia as well as neurons, an apparent “new neuron” signal may be reflecting glial turnover instead.²⁸ Results still vary across labs and methods, but the cautious middle ground has held. Adult hippocampal neurogenesis in humans may exist, but it’s likely limited, hard to detect reliably, and easy to overestimate when the markers do not perform a single, specific function.

Do brain-training games produce meaningful neuroplasticity?

Brain-training games sell a simple promise: play a few minutes a day and attention, memory, and reasoning will improve through neuroplasticity. That promise got loud enough that, in 2014, two public letters from scientists landed on opposite sides of the debate.^29,30 One letter from the Stanford Center on Longevity and the Max Planck Institute argued that there wasn’t compelling evidence that brain-training games reduce or reverse cognitive decline.²⁹ A response letter pushed back from a different team of researchers, saying there was a substantial and growing body of evidence for certain training programs, while still conceding that marketing claims were often exaggerated.³⁰

Regulators noticed the gap between claims and evidence as well. Lumosity, one of the largest brain-training apps, has reported more than 100 million users, and the broader brain-training market has grown into a multibillion-dollar industry.³¹ In 2016, the Federal Trade Commission announced a $2 million settlement with Lumosity over advertising that promoted broad real-world benefits without adequate scientific support.³² Underneath the headlines sits a real scientific question. If practice can change neural circuits, what kind of change is being produced by these games, and how far does it travel beyond the screen?

Most disagreements come down to how neuroplasticity is defined in practice. Repetition can strengthen a circuit, yet that doesn’t guarantee the benefit will generalize to other tasks or skills. To distinguish between these phenomena, researchers often separate “near transfer” from “far transfer.” Near transfer means getting better at the exact skill being trained or a closely related one, like improving on a working-memory game and then doing better on another memory task.³³ Far transfer is the bigger claim, where the new wiring is supposed to carry over to broader abilities like reasoning, school or job performance, or daily decision-making.

A meta-analysis of meta-analyses in 2019 found that working-memory brain training games were linked with near-transfer gains, but far-transfer effects were small or null. When placebo effects and publication bias were accounted for, the overall far-transfer effect and “true variance” were estimated at zero.³³ That doesn’t mean there’s no plastic change at all; rather, it indicates the change is likely to be rather narrow, tuned to the trained task, and far less likely to show up where the marketing says it does.

Psychedelics and “rapid neuroplasticity” as psychiatric treatment

A growing narrative around psychedelics is that they may promote rapid neuroplasticity, sometimes described as “psychoplastogenic” effects.³⁴ While the existence of plasticity in general isn’t in question, the controversy is whether psychedelics reliably produce rapid, large structural rewiring in humans, rather than mostly smaller short-term changes in brain activity and experience.

In pre-clinical work, the story reads more clearly. In rodents and lab-grown neurons, psychedelics have been linked to higher levels of plasticity-related signals such as brain-derived neurotrophic factor (BDNF), a growth factor often described as “fertilizer” for synapses, and activity markers like c-Fos, which flag that neurons have recently been engaged.³⁵ Several studies have also reported increases in dendritic spine density, meaning a greater number of the tiny bumps where many synapses form and, in turn, more potential sites for neural connections.

The gap, however, shows up when the same question is asked in humans. Most clinical studies examining the neuroplastic effects of psychedelic therapies lean on functional magnetic resonance imaging (fMRI) connectivity, which can show that networks are communicating differently, but it can’t confirm whether new synapses have formed or whether new neurons have been generated.³⁶ Study design complicates interpretation too. Samples are often small, and true placebo control is hard because participants can usually tell whether they’ve received a psychedelic, and expectations can shape both reported symptoms and behavior.³⁷ With those limits, it’s harder to decide whether observed changes reflect durable plastic remodeling or a powerful temporary state, and it’s also harder to link biology to lasting behavioral outcomes.

Braille reading and visual cortex reorganization

Braille reading provides another very clean demonstration of human neuroplasticity, because the brain’s visual system can end up supporting touch. In a landmark positron emission tomography (PET) study, Norihiro Sadato and colleagues scanned blind, proficient Braille readers and sighted controls during tactile discrimination tasks.³⁸ PET is a brain-imaging method that tracks metabolic activity, essentially indicating which regions are working harder. In the blind group, the occipital cortex, the back-of-the-brain region usually used for vision, was activated during Braille-related touch processing, while sighted participants showed reduced activity in visual regions during similar touch tasks. A simple touch condition without discrimination demands didn’t produce the same effect, suggesting the response was tied to skilled reading rather than touch alone.

Later work extended the idea to sighted adults who had learned Braille, which helps separate training effects from blindness itself.³⁹ Visual cortex activity increased during tactile reading, and connectivity between visual and touch systems strengthened. The visual word form area, a visual region usually involved in recognizing written words, was recruited more strongly in faster readers. When TMS was applied, a noninvasive method that uses magnetic pulses to briefly disrupt a targeted brain region, tactile reading accuracy dropped. In these studies, function appeared to follow use, and visual brain tissue was recruited for a new sensory skill.

London taxi drivers and spatial navigation

London taxi drivers train for years to pass “The Knowledge,” a memorized map of thousands of streets and landmarks. In an magnetic resonance imaging (MRI) study, Eleanor Maguire and colleagues compared licensed taxi drivers with control participants and reported a consistent pattern in the hippocampus, a deep brain structure involved in forming spatial memories.⁴⁰ The taxi drivers showed greater volume in the posterior hippocampus and smaller volume in the anterior hippocampus, and time spent driving was linked to the same back-to-front pattern. MRI can’t measure learning directly, but it can estimate tissue volume, so the findings suggested that long-term navigation experience may be associated with measurable structural differences.

A later study strengthened the design by following London taxi driver trainees over time.⁴¹ Trainee drivers were scanned before training and then scanned again several years later, alongside controls. Only some trainees qualified as licensed taxi drivers, and at baseline the groups didn’t differ in hippocampal gray matter. By follow-up, the trainees who qualified showed increased gray matter in the posterior hippocampus, while those who didn’t qualify and the controls didn’t show the same change. Put side by side, the studies suggest the hippocampus may change with training, rather than the drivers simply starting out different.

Distributed Practice

Some learning strategies align with neuroplasticity better than others, and distributed practice is one of the most studied. It spaces practice across multiple time points, so information is revisited after a delay rather than repeated immediately. Read on for how spacing supports long-term retention, and what it looks like in real study or training plans.

Memory Consolidation

Memory consolidation is the brain process that stabilizes new learning over time, turning short-term traces into longer-lasting memories, which is one way neuroplasticity manifests. This piece explains how memories are “set,” how long that process can take, and what can happen when a memory is reactivated.

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