In recent decades, neuroscientists have come to a “slow realization that dendritic activity is super important for plasticity and for neuronal computations in general,” said Antoine Madar, a postdoc at the University of Chicago, who led the 2025 review of a Society for Neuroscience symposium on BTSP in The Journal of Neuroscience.

There is a “zoo” of different events that take place at dendrites, he said. They can fire their own local or global electrical spikes. They can cover a larger or smaller area, and they can surge for longer or shorter periods of time. Neuroscientists have found that these events at dendrites can allow even single neurons to perform complex computations — meaning that dendrites are the reason why a single neuron can have the same amount of computational power as a deep artificial neural network.

Still, there was much unknown about dendrites’ behavior. Neuroscientists have mainly characterized them in brain slices, where neurons are alive and can be activated but aren’t attached to a living animal. “We were trying to take that into the actual behaving animal, or the actual behaving brain,” Magee said.

In 2014, they began to home in on the hippocampus, an especially plastic area of the brain where we form experiential memories. It’s also home to place cells, which fire when an animal moves through its environment. Each of these neurons learns to fire at specific locations; later, if the rodent reenters that place, the cell will fire, recalling relevant information stored in the network.

As the rodents ran on a circular track, Magee and his team recorded what was happening in their hippocampal dendrites. That’s when they observed something interesting.

Neuroscientists had long known that dendrites can sometimes stay active, with a slightly higher charge than when they’re resting, for long periods of time without firing — creating what’s known as a plateau potential. Because a plateau potential increases the odds that the neuron will fire, the activity was considered important to neuroplasticity. But while examining the rodent data, Bittner saw that place cells whose dendrites had produced just a single plateau potential began to fire.

In other words, a single burst of activity at the dendrite had tuned that cell to fire in that location. It was previously thought that encoding a place cell would take multiple action potentials, via Hebbian learning, which would require the animal to explore the same spot multiple times.

“So we were like, ‘Wow, what’s going on here?’” Magee said. When they experimentally triggered these plateaus, the cells fired in that location 99.5% of the time after a single dendritic plateau.

The researchers were elated. “We were kind of running back and forth between offices, like, you know, waving papers around — like, ‘Look at this result,’” said Aaron Milstein, a neuroscientist at Rutgers University, who worked in Magee’s lab at the time. It seemed that dendrites weren’t just passively nudging a neuron to fire — they were causing the change themselves, strengthening the synapse in a single, swift step.

Magee and his team published their findings in 2015. At that point, they thought they had observed some weird subtype of Hebbian plasticity. But when they looked more closely at brain recordings of live animals plus brain slices, they recognized the biggest difference between the dendrites’ activity and Hebbian plasticity: time.

In most studies of Hebbian plasticity, neurons can strengthen or weaken their connection if they are activated within milliseconds of each other. Dendrites’ plateau potentials, on the other hand, persist for tens to hundreds of milliseconds (sometimes approaching one second), and through BTSP they can strengthen synapses active six to eight seconds before or after the plateau event.

“It became pretty obvious that this wasn’t at all the standard kind of Hebbian plasticity,” Magee said. “That made it even more interesting, of course, and a little bit intimidating, because then we were going to be facing up to nearly 100 years’ worth of dogma.”

It also addressed another big question that Hebbian plasticity had left open: how our cells can capture our relatively slow human behaviors.

“If you imagine even the simplest of the behavioral learning — for example, learning to stop at a red light signal, or to even explore and figure out what are the main parts in a particular room — it will take you at least a few seconds,” said Anant Jain, a neurophysiologist at the Center for High Impact Neuroscience and Translational Applications in India. BTSP explains how the brain can encode behaviors in a single burst of brain activity that unfolds across several seconds.

Because this new mechanism seemed more behaviorally relevant than Hebbian learning, Magee named it “behavioral time scale synaptic plasticity” in a 2017 Science paper. “I’m not very good at naming things,” he admitted. Then he waited for the response from fellow neuroscientists.

One-Shot Learning

Initially, BTSP received pushback within the field. There was good reason for that, Magee said, as it challenged the dogma of neuroplasticity that had dominated for decades. But over the past few years, other researchers have started to investigate it themselves.

This is “a very compelling model for single-shot learning,” said Losonczy, who worked in Magee’s lab prior to the discovery and now studies BTSP at his lab. Unlike the mechanisms that allow an animal to learn a new skill slowly, BTSP might help it to learn — after just a single exploration of its cage — that food exists in the northwest corner or that a shock exists to its south. “Sometimes you need to remember events you only have one chance to remember, [such as] where the predator is,” Losonczy said. “Otherwise, you will be taken out of the genetic pool.”

While it’s a neat explanation, the exact mechanism remains elusive. “There are still so many unanswered questions, at least at the level of molecules,” Jain said. However, neuroscientists are starting to get some hints.

Early findings suggest that certain experiences cause synapses, the gaps between neurons where dendrites extend, to be tagged with elusive biochemical signatures called eligibility traces. These tags stick around for several seconds and indicate that those neurons were recently active and therefore relevant to a particular experience. Then, in the next neuron, a dendritic plateau potential causes a widespread voltage change that spreads across the entire dendrite. This plateau triggers all the synapses with the eligibility trace to strengthen.

Some studies are starting to zoom in on the molecular process. In 2024, Jain and his team reported that dendritic plateaus might cause a cascade of biochemical signals to build up over several seconds and then activate one of the most important proteins for learning, known as CaMKII. This protein directly influences synaptic strength by physically increasing the surface area and the number of receptors on dendrites, allowing more neurotransmitters to bind there the next time the cell fires.

BTSP may also address an ongoing conundrum in neuroscience. Because it strengthens only relevant active neurons, as opposed to any active neuron, BTSP may help address the “credit assignment problem” — how the brain can tell which neurons should encode a given experience. Now, Magee and others are looking into the role that BTSP might play not only in learning but also in consolidating memories.

However, Dombeck is cautious about overreaching on BTSP’s significance. It has been observed in limited circumstances: only in the hippocampus as an animal learns locations (although researchers have found some evidence for BTSP in the neocortex, where the brain’s higher-order processes happen). In his lab, Dombeck has found that BTSP occurs in some hippocampal cells, but not in all of them.

Jain is not even convinced that BTSP should be categorized as a non-Hebbian type of learning. Hebbian learning is often vaguely defined, and Hebb himself was vague about the timescales upon which it works. “Donald never really specified that it has to happen within milliseconds,” only that the neurons need to repeatedly fire together, he said. Only later did neuroscientists mechanistically refine it to include millisecond timescales, Jain said.

Most neuroscientists agree that BTSP doesn’t replace Hebbian learning, but rather works alongside it. “Hebbian plasticity probably plays a huge role in development, in the initial wiring” of the brain, Grienberger suggested, while BTSP may be more important for forming episodic memories in adults.

There’s still much unknown about BTSP, especially the mechanism, which Madar said is “quite speculative.” However, he also acknowledged that before becoming the archetypal model for learning, “Hebbian plasticity was also a hypothesis.” Our understanding of how the brain learns through endlessly changing is itself endlessly changing.