一种新型神经可塑性：单次经历即可重塑大脑
内容总结：
研究发现新型神经可塑性机制：一次经历即可重塑大脑
一项发表于《神经科学杂志》和《自然·神经科学》的综述研究揭示了一种全新的神经可塑性形式——"行为时间尺度突触可塑性"（BTSP）。与传统的赫布可塑性不同，这种机制允许大脑仅通过单次体验即可完成学习，而非依赖重复刺激。
传统观点认为，大脑的学习需要神经元在毫秒级时间窗口内反复同步放电，即"一起放电的神经元连在一起"。然而，美国布兰迪斯大学的神经科学家克里斯蒂娜·格林伯格及其同事发现，海马体中的树突能够产生持续数百毫秒的"平台电位"，并在长达6-8秒的时间窗口内强化此前或此后活跃的突触连接。这一发现解释了为何人类能够通过单次经历——如触碰热炉——立即形成记忆。
研究团队最初在2014年对啮齿动物进行实验时偶然发现了这一现象。当动物在圆形轨道上奔跑时，海马体中的位置细胞仅需一次树突平台电位即可编码特定位置信息，成功率高达99.5%。这与传统赫布可塑性要求多次重复学习完全不同。
目前，科学家正试图揭示BTSP的分子机制。初步研究表明，特定经历会在活跃突触上留下"资格痕迹"化学标签，持续数秒；随后树突平台电位引发广泛电压变化，强化带有该标签的突触连接。这一过程可能涉及关键学习蛋白CaMKII的激活，该蛋白能增加树突表面受体数量，从而增强信号传递效率。
尽管BTSP在神经科学界最初遭遇质疑，但如今已被视为"单次学习"的重要模型。研究指出，赫布可塑性可能主要负责大脑发育期的初始连接，而BTSP则在成年人的情景记忆形成中发挥关键作用。专家表示，这一发现填补了神经可塑性研究领域的长期空白，为理解大脑如何从经验中学习提供了全新视角。
中文翻译：
一种新型神经可塑性：单次经历即可重塑大脑
引言
每一次经历都会改变我们的大脑，如同陶艺家重塑一块黏土。每一次转角、每一次交谈、每一次战栗都会引发连锁反应：化学物质释放，电流涌动，脑细胞间的连接增强，我们的心智模型随之更新。
布兰迪斯大学神经科学家克里斯汀·格林伯格表示，大脑“具有惊人的可塑性，并且这种特性会贯穿人的一生”。这种可塑性——即易于重塑的特质——使大脑极为擅长学习，而学习正是让我们能记住小说情节、探索陌生城市、掌握新语言、避免触碰热炉灶的关键过程。然而，神经科学家仍在探索描述神经可塑性如何重塑大脑连接的基本规律。
近期，神经科学家描述了一种新型神经可塑性，它可能帮助大脑在几秒钟的时间尺度上学习——这个时长足以捕捉单次经历中的学习行为过程。在发表于《神经科学杂志》和《自然·神经科学》的两篇综述中，他们提出了“行为时间尺度突触可塑性”（BTSP）。这种发生在大脑海马体（记忆中枢）的学习方式，由一次同时影响多个神经元、持续数秒的电变化引发。研究人员推测，它可能帮助大脑通过单次尝试完成学习。
“很明显，（BTSP）是一种强大有力的机制，能促成即时记忆的形成，”西北大学神经科学家丹尼尔·多姆贝克（未参与该理论发展）说，“这是该领域长期以来缺失的一环。”
通过揭示BTSP，神经科学家进一步揭开了大脑如何随经历变化的奥秘，让我们更接近理解学习的本质。“神经可塑性是……大脑最后的边疆之一，”得克萨斯大学西南医学中心研究BTSP的神经科学家阿提拉·洛松齐说，“如果我们理解了它，我认为我们将朝着理解大脑工作原理迈出重要一步。”
可塑的大脑
如今，神经可塑性已被视为事实，但在神经科学150年历史的大部分时间里，成年大脑曾被认为是一成不变的。“成年大脑可以改变这一观点，直到现代神经科学史的后期才被广泛接受，”受过专业训练的神经科学家、《神经可塑性》（MIT出版社入门读物）作者穆赫布·科斯坦迪说，“人们曾理所当然地认为成年人类大脑无法改变。”1928年，常被引用的现代神经科学奠基人圣地亚哥·拉蒙·卡哈尔写道：“在成年中枢神经中，神经通路是固定、终结、不可改变的。”这一观点一直盛行至20世纪中期。
（配图：圣地亚哥·拉蒙·卡哈尔/公共领域）
我们现在知道，大脑在功能和结构上都在不断重塑自身，尺度各异——从神经元间流动的分子，到横跨大脑乃至更广范围的连接。
神经可塑性的力量或许最好通过案例研究来证明。一位天生没有嗅球的患者能够闻到气味，因为她大脑的其他部位重塑后充当了替代结构。另一位患者在婴儿时期被切除了整个左脑；在其右脑重组并承担左脑原有功能后，如今她过着正常的生活。当中风或事故损伤大脑时，其他神经元会填补进来，帮助患者恢复说话、行走等日常功能。
神经可塑性也驱动着日常学习。这一过程主要被认为源于突触可塑性，即神经元之间数以万亿计连接的改变。尽管大脑有多种学习方式，但一个特定观念已主导了70多年。
1949年，加拿大心理学家唐纳德·赫布阐述了一种学习理论，即现在所称的赫布可塑性。根据这一模型，当神经元在彼此激活的毫秒级时间差内同步放电时，它们之间的连接会物理性增强，使得未来它们更倾向于共同激活。久而久之，它们会形成一个代表某一概念或经历的神经网络。换言之，大脑中的网络用得越多就越强，这一观念常被总结为“同步放电的神经元，会连在一起”。
（配图：UBC档案照片集；不列颠哥伦比亚大学图书馆档案部）
但神经科学家“一直隐约怀疑赫布可塑性并不完全正确”，贝勒医学院神经科学家杰弗里·马吉说。至少，它并非故事的全貌。它要求经历被重复多次才能在大脑上留下印记——这一框架或许能解释我们如何学会探索新城市或掌握新语言，却无法解释我们如何从单次、高度刺激的经历（如触碰热炉灶）中学习。
即便如此，寻找更具解释力的机制并未成为神经科学家关注的重心。“这不像粒子物理学寻找缺失粒子那样是一场探索，”洛松齐说。或许需要填补一些空白，但大多数研究人员认为赫布框架只需微调。很少有人想到，对神经可塑性的更全面理解可能包含一种新机制。
强大的树突
2014年，马吉将电极连接到啮齿动物身上记录神经活动时，并非意在挑战赫布可塑性。当时在霍华德·休斯医学研究所珍妮利亚研究园区工作的马吉，及其学生格林伯格和凯蒂·比特纳，正试图观察活体动物中神经元“手臂”——树突——的行为。
这些分支在神经元一端接收分子信号，并诱导细胞快速释放电信号，该信号沿胞体传播，称为动作电位。这一过程以神经元释放自身的一批分子信号结束，这些信号会附着到网络中下一个神经元的树突上，延续整个过程。
（配图：Jose Calvo/Alamy）
近几十年来，神经科学家已“逐渐认识到树突活动对可塑性乃至一般神经元计算至关重要”，芝加哥大学博士后安托万·马达尔说。他主导了2025年《神经科学杂志》上关于BTSP的神经科学学会研讨会综述。
他表示，树突上发生着“各式各样”的不同事件。它们可以激发局部或全局的电尖峰。这些尖峰可覆盖大小不同的区域，持续时间也有长有短。神经科学家发现，树突上的这些事件甚至能让单个神经元执行复杂的计算——这意味着，树突是单个神经元拥有与深层人工神经网络同等计算能力的原因。
尽管如此，关于树突的行为仍有很多未知。神经科学家主要是在脑切片中研究它们——这些切片中的神经元虽存活并可被激活，但并未连接在活体动物身上。“我们试图将这一研究推进到实际活动的动物，或者说实际活动的大脑中，”马吉说。
2014年，他们开始聚焦海马体——大脑中特别具有可塑性、负责形成经历记忆的区域。这里也是位置细胞的所在地，当动物在环境中移动时，这些细胞会放电。每个位置细胞学会在特定位置放电；之后，如果啮齿动物再次进入该位置，该细胞便会放电，调取存储在网络中的相关信息。
（配图：杰弗里·马吉供图）
当啮齿动物在圆形轨道上奔跑时，马吉团队记录了它们海马体树突中的活动。就在这时，他们观察到了有趣的现象。
神经科学家早已知道，树突有时能长时间保持活跃状态（电荷略高于静息态）而不放电——形成所谓的“平台电位”。由于平台电位会增加神经元放电的概率，这一活动被认为对神经可塑性很重要。但在检查啮齿动物数据时，比特纳发现，树突仅产生一次平台电位的位置细胞便开始放电。
换句话说，树突上的一次单一爆发活动，便调谐了该细胞在那一位置放电。先前认为，通过赫布学习，编码一个位置细胞需要多次动作电位，而这需要动物多次探索同一位置。
“所以我们当时想，‘哇，这到底是怎么回事？’”马吉说。当他们实验性地触发这些平台电位时，在单次树突平台电位后，这些细胞在99.5%的情况下会在该位置放电。
研究人员欣喜若狂。“我们在办公室之间来回奔跑，挥舞着论文——就像，‘快看这个结果，’”当时在马吉实验室工作的罗格斯大学神经科学家亚伦·米尔斯坦说。树突似乎并非被动地推动神经元放电——它们自身正在引发改变，以单步、快速的方式增强突触。
马吉团队于2015年发表了他们的发现。当时，他们认为自己观察到了赫布可塑性的某种奇怪亚型。但更仔细地分析活体动物脑部记录及脑切片后，他们意识到了树突活动与赫布可塑性最大的区别：时间。
在大多数赫布可塑性研究中，如果神经元在彼此激活的毫秒级时间差内同步放电，其连接可以增强或减弱。而树突的平台电位则能持续数十到数百毫秒（有时接近一秒），通过BTSP，它们可以增强在平台事件发生之前或之后六到八秒内活跃的突触。
“很明显，这完全不是标准的赫布可塑性，”马吉说，“当然，这让它更有趣，也略显棘手，因为我们将要面对近百年来的教条。”
它还解决了赫布可塑性留下的另一个大问题：我们的细胞如何捕捉我们相对缓慢的人类行为。
“想象一下最最简单的行为学习——例如，学会在红灯前停车，甚至只是探索并弄清某个房间的主要部分——这至少需要几秒钟，”印度高影响神经科学与转化应用中心神经生理学家阿南特·贾因说。BTSP解释了大脑如何通过一次持续数秒的脑活动爆发来编码行为。
由于这种新机制似乎比赫布学习更具行为相关性，马吉在2017年的《科学》论文中将其命名为“行为时间尺度突触可塑性”。“我其实不太擅长起名字，”他承认。随后他等待了同行神经科学家的反应。
一次性学习
最初，BTSP在领域内受到了抵制。马吉说，这有充分理由，因为它挑战了主导数十年的神经可塑性教条。但过去几年里，其他研究人员已开始自行研究它。
这是“一个极具说服力的单次学习模型”，洛松齐说。他在该发现前曾在马吉实验室工作，如今在自己的实验室研究BTSP。与让动物缓慢学习新技能的机制不同，BTSP或许能帮助动物——在仅仅探索一次笼子后——就学会食物在西北角或电击在南方。“有时你需要记住只有一次机会回忆的事件，（比如）捕食者在哪里，”洛松齐说，“否则你就会被淘汰出基因库。”
尽管解释得通，但确切机制仍不明朗。“仍有太多未解之谜，至少在分子层面是这样，”贾因说。不过，神经科学家已开始获得一些线索。
初步发现表明，某些经历会导致突触（树突延伸的神经元间隙）被贴上称为“资格痕迹”的隐秘生化标签。这些标签会持续数秒，表明那些神经元最近活跃过，因此与特定经历相关。随后，在下一个神经元中，一个树突平台电位会导致广泛的电压变化，扩散至整个树突。这一平台电位会触发所有带有资格痕迹的突触增强。
一些研究已开始聚焦分子过程。2024年，贾因团队报告称，树突平台电位可能引发一连串生化信号在数秒内积累，然后激活一种对学习至关重要的蛋白质——CaMKII。这种蛋白质通过物理增加树突的表面积和受体数量，直接影响突触强度，使得下次细胞放电时能结合更多神经递质。
BTSP还可能解决神经科学中的一个持续难题。由于它只增强相关的活跃神经元（而非任何活跃神经元），BTSP或有助于解决“信用分配问题”——大脑如何判断哪些神经元应编码特定经历。如今，马吉等人正在研究BTSP不仅在学习中，也可能在记忆巩固中发挥的作用。
然而，多姆贝克对过度夸大BTSP的意义持谨慎态度。它仅在有限条件下被观察到：仅在海马体中，且仅当动物学习位置时（尽管研究人员已在新皮层（大脑高级处理区域）发现一些BTSP的证据）。在他的实验室中，多姆贝克发现BTSP发生在部分海马细胞中，但并非全部。
贾因甚至不确定BTSP是否应被归类为非赫布型学习。赫布学习的定义往往模糊，赫布本人对其工作的时间尺度也表述含糊。“唐纳德从未具体说明它必须在毫秒级发生，”仅提到神经元需要重复同步放电，他说。贾因表示，只是后来神经科学家才在机制上将其细化到毫秒级时间尺度。
大多数神经科学家同意BTSP并未取代赫布学习，而是与之协同工作。“赫布可塑性可能在发育过程中，在大脑的初始布线中发挥巨大作用，”格林伯格提出，而BTSP可能对成年人形成情景记忆更为重要。
关于BTSP仍有大量未知，尤其是其机制，马达尔称其“相当推测性”。不过，他也承认，在成为学习的典型模型之前，“赫布可塑性也只是一个假说”。我们对大脑如何通过无尽变化来学习的理解，本身也在无尽变化中。
英文来源：
A New Type of Neuroplasticity Rewires the Brain After a Single Experience
Introduction
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, 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 and Nature Neuroscience, 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.”
By uncovering BTSP, neuroscientists have unraveled more of the story of how the brain changes with experience, bringing us closer to understanding how learning happens. “Neuroplasticity is … one of the last frontiers of the brain,” said Attila Losonczy, a neuroscientist at the University of Texas Southwestern Medical Center who studies BTSP. “If we understand this, I think we take a major step towards understanding how the brain works.”
A Plastic Brain
Today, neuroplasticity is taken as fact, but for much of the 150-year history of neuroscience, the adult brain was thought to be static. “The idea that the adult brain can change wasn’t actually widely accepted until very late [in] the history of modern neuroscience,” said Moheb Costandi, a trained neuroscientist and author of Neuroplasticity, a primer from MIT Press. “It was taken for granted that the adult human brain can’t change.” In 1928, Santiago Ramón y Cajal, the oft-cited founder of modern neuroscience, wrote that “in adult centers the nerve paths are something fixed, ended, immutable.” This idea would prevail well into the middle of the 20th century.
Santiago Ramón y Cajal/Public Domain
We now know that the brain is constantly remolding itself, both functionally and structurally, across many scales — from the molecules that flow between neurons to the connections that stretch across the brain and beyond.
The power of neuroplasticity is perhaps best demonstrated by case studies. One patient born without an olfactory bulb could smell because other parts of her brain remolded to serve as substitutes. Another patient had the entire left side of her brain removed as a baby; after her right side reorganized to take on the left’s former roles, today she has a functional life. When a stroke or an accident damages the brain, other neurons fill in to recover patients’ everyday functions such as speaking and walking.
Neuroplasticity also drives everyday learning. This process is mainly thought to result from synaptic plasticity, or changes to the trillions of connections between neurons. And although the brain learns in various ways, one particular idea has dominated for more than 70 years.
In 1949, Donald Hebb, a Canadian psychologist, articulated a theory of learning now known as Hebbian plasticity. According to this model, when neurons are activated within milliseconds of each other, the connection between them is physically strengthened, so that in the future they are more likely to fire together. Over time, they form a network that represents a concept or an experience. In other words, the more the networks in the brain are used, the stronger they get, an idea often summarized as “neurons that fire together, wire together.”
UBC Archives Photograph Collection; University Archives, University of British Columbia Library. UBC 41.1/2039-1
But neuroscientists “always had a sneaking suspicion that Hebbian plasticity wasn’t quite right,” said Jeffrey Magee, a neuroscientist at Baylor College of Medicine. Or at least, it wasn’t the full story. It required an experience to be repeated multiple times to imprint the lesson on the brain — a framework that may explain how we learn a new city or language, but not how we learn from a single, highly charged experience, such as touching a hot stove.
Even so, finding more explanatory mechanisms hasn’t been top of mind for neuroscientists. “It wasn’t a quest, like in particle physics for missing particles,” Losonczy said. Maybe there were a couple of gaps that needed to be filled, but most researchers assumed that the Hebbian framework would require only tweaks. Few were thinking that a fuller understanding of neuroplasticity might include a new mechanism.
Mighty Trees
In 2014, when Magee attached electrodes to rodents to record their neural activity, he wasn’t looking to challenge Hebbian plasticity. Magee, then at the Howard Hughes Medical Institute’s Janelia Research Campus, and his students Grienberger and Katie Bittner were looking to observe the behavior of neurons’ arms, called dendrites, in a living animal.
These branches receive molecular signals at one end of a neuron and induce the cell to rapidly fire an electrical charge that ripples down the cell body, known as an action potential. This process ends with the neuron releasing its own batch of molecular signals, which latch onto the dendrites of the next neuron in the network, continuing the process.
Jose Calvo/Alamy
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.
Courtesy of Jeffrey Magee
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.