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- A major study discovered that individual synapses on the same neuron can learn in different ways.
- Mice with altered genes allowed scientists to watch thousands of synapse changes in real-time as learning happened.
- This changes our understanding of memory and brain function, showing neurons are more complex than we thought.
- Identifying different learning patterns may help create better treatments for mental health issues.
- These ideas could inspire AI systems that copy the brain’s varied and specialized learning methods.
Every time you learn or recall something, your brain is working hard to change its networks. But new proof shows that brain learning is much more complex and amazing than we realized. A major neuroscience discovery has shown that neurons don’t all learn the same way. Instead, they learn using different methods, sometimes at the same time within one cell. This changes basic ideas in neuroscience and could affect everything from mental health care to artificial intelligence.
The Basics: How the Brain Learns and Makes Memories
Learning starts with the structure and behavior of the brain’s roughly 86 billion neurons. Neurons talk to each other using synapses, tiny gaps where chemicals move between cells. Information moves across this network through electrical pulses and chemical signals.
A neuron has many parts, but dendrites are very important for learning. These branch-like parts receive signals from other neurons. Learning happens when some synapse connections between these dendrites and nearby neurons get stronger or weaker. This ability to adapt, called synaptic plasticity, is the biological basis of memory and learning. Each experience changes this neural network a little, changing how we see, think, and react to the world.
Synaptic plasticity allows for a range of flexibility: making important synapses stronger (called long-term potentiation, or LTP) and weakening others (known as long-term depression, or LTD). This changing network lets each new memory, skill, or thought get coded in the brain’s physical design. Without synaptic plasticity, learning would not be possible.
The Old Model: Hebbian Learning Explained
For almost a century, our main idea of how neurons learn came from a simple concept first suggested in the 1940s by psychologist Donald Hebb. His model, now called Hebbian learning, is often simplified to: “neurons that fire together, wire together.”
Here’s how it works: when a neuron before the synapse often turns on a neuron after the synapse, the link between them gets stronger, making their communication better. This idea made sense and fit with what was seen in animal studies and computer models.
The Hebbian model affected both neuroscience and artificial intelligence. In fact, many of today’s machine learning methods are based on Hebbian ideas. But, one main idea of this theory is now being questioned: that all synapses on a neuron learn the same way, only based on their timing and activity levels.
Hebbian learning doesn’t consider that neurons get tens of thousands of different signals, and many of these suggest different situations and meanings. Using one learning rule for all synapses could cause overgeneralization, mistakes, and even system overload.
Solving the Credit Assignment Problem
This leads to a big unsolved question in neuroscience: how does the brain decide which synapse connections to change during learning? This is famously known as the “credit assignment problem.” When something happens—like solving a puzzle or making a mistake—how does the brain know which actions or signals helped cause that outcome?
If all incoming signals are treated the same, then useful ones might be missed, and unimportant ones might be made stronger. In the Hebbian model, all active synapses at the same time are seen as contributors. But in reality, not all signals are equally important.
This problem has puzzled neuroscientists for many years. A solution needs watching things in real-time at a tiny level, and until recently, that was not possible with technology. But now, because of advances in imaging and genetic tagging, scientists can watch individual synapses change as an animal learns.
A Closer Look: The Study That Changed Everything
The discovery came in a 2024 study in Science by Bittner and Magee. Using advanced imaging tools and genetic changes, researchers closely watched thousands of synapses on neurons in live mice brains as they learned to press a lever for water.
The researchers used genetically coded fluorescent sensors that could track neuron activity down to a single synapse. These sensors lit up with color when activity happened at a synapse, letting scientists record and study the real-time changes during learning behavior.
This was not just another brain scan or general neuron reaction. The team looked at dendritic spines—tiny bulb-like parts where synapses form—over time as the mice did the task many times. This let the researchers see not just if neurons were active, but how individual signals changed, differed, or stayed the same during the learning process.
Key Discovery: Neurons Use Multiple Learning Rules at Once
Here’s the discovery that surprised the neuroscience world: the researchers saw that different dendritic spines on the same neuron were using different learning rules. While some did follow the Hebbian idea—getting stronger when both neurons before and after the synapse were active at the same time—others changed separately from the main neuron’s output.
In short, this study showed that learning is not the same within each neuron. Some synapses used one type of activity-based change, while others reacted to different stimuli that might be local or specific to the signal. One neuron can then use several specialized learning methods at the same time, greatly increasing its ability to represent information (Bittner & Magee, 2024).
This suggests that individual synapses themselves have more freedom and complexity than we previously thought. It also means that you’re not working with one brain-wide learning method but thousands of tiny, specific learning rules running at the same time.
Why This Matters: Neurons Multitask
Think of this in everyday examples. Let’s say you’re learning to play a piano song. One group of neurons might focus on your finger placement, another on what you hear, and another on rhythm. If each of these learning types needs different training signals, it makes more sense for the neuron getting the signals to use different teaching methods for each signal.
The neurons themselves act like complex centers that combine information. By handling different learning patterns across their dendrite branches, they can best process and store related and different streams of information.
This discovery greatly improves our understanding of how the brain learns, showing it not as a simple cause-and-effect system but as a complex, layered, and very specific structure. It suggests that memory storage and learning happen not just across networks, but also within single cells.
What This Tells Us About Memory and Brain Function
This greatly changes our model of memory and brain function. Instead of thinking of memory as one process that updates the brain’s connections in the same way for new information, it now seems to be a coordinated but divided process. Different parts of a neuron’s dendrite tree may separately code different parts of a memory or learning task.
The effects are big
- Memory making might be stronger against damage, because it is spread across small areas.
- Certain brain skills, such as seeing patterns or context, could be linked to different synapse routines.
- Brain efficiency increases by allowing small changes without changing whole circuits.
Moreover, recent studies show that some brain functions depend a lot on small details in synapse behavior. For example, context memory is thought to depend on “tagging” specific synapse areas with chemical markers—something easier to understand with this multi-rule idea (Takeuchi et al., 2014).
Rethinking Brain Disorders: What it Means for Mental Health
A better understanding of how the brain actually learns opens new ways to understand and treat brain and mental disorders. Disorders like depression, schizophrenia, and autism are already linked to problems with synapse control, often called “synaptopathies” (Grant, 2018).
If different learning rules work at the same time within neurons, then problems with one type of learning in specific synapse areas could show up as very specific symptoms—for example, not feeling pleasure (anhedonia) in depression or trouble recognizing social cues in autism.
This could explain why some mental health disorders don’t respond to general treatments. Instead of targeting chemicals across the whole brain, future treatments could try to fix or change learning problems in specific types of synapse behavior.
In depression, for example, weaker excitatory synapse strength in the front of the brain has been linked to less motivation and reward feeling. Understanding which specific learning rule is not working could help create treatments that only target those rules that are not working right (Duman & Aghajanian, 2012).
Uses in AI: Making Neural Networks More Human
Artificial intelligence today still uses simple neural network models where learning rules are the same for all “connections.” This discovery offers a big change: adding different learning rules into AI could improve their ability to handle unclear, noisy, or overlapping information.
AI methods that are based on biology like spiking neural networks or capsule networks already try to copy some complexities of the brain. Adding the idea of different learning ways within a single network node—or “neuron”—could allow machines to handle more changing situations with fewer mistakes.
Moreover, AI that copies human-like parallel processing might soon be able to do different tasks at the same time (like a human learning a concept while also dealing with new emotions), improving general intelligence and ability to adapt (Richards et al., 2019).
What This Means for Education and Cognitive Training
Understanding how the brain learns also has clear effects on how we teach, coach, and train people. If individual synapses learn differently even within one brain cell, then education that is specific to each person becomes even more important.
This supports the idea of education that uses many ways of learning which uses audio, visual, movement, and context at the same time. Moreover, well-planned repetition, emotionally important context, and different ways of showing information could help involve different neural systems better.
Programs for brain training and learners with differences in brain function (e.g., students with ADHD or dyslexia) could gain a lot from these discoveries. Instead of repeating the same information in the same way, changing the signals coming in may work better with the different learning methods of the brain.
Remaining Questions: What’s Still Unknown?
While the study found major new truths, it also brought up new questions
- What controls which synapse uses which learning rule?
- Are these choices built-in from genes, or learned from the environment?
- Can the brain change rules over time depending on the situation or need?
To answer these, scientists will need even better tools and computer models. But answering these questions could further explain how individual neurons shape consciousness, memory combination, and behavior.
Neuroscience Meets Technology: New Tools That Made This Possible
This big step in understanding is also thanks to modern scientific tools. Genetically changed animal models, fluorescent activity sensors, and very high quality microscopes allowed watching a working brain learn in real-time at a tiny level.
These are not just fancy tools—they are bringing neuroscience into a new time where we can map learning across time, place, and even individual synapses. As these tools get even more exact, we can expect many more discoveries in the coming years.
Summary: A New Neural Story Appears
This new picture of how the brain learns is both amazing and makes you feel small. Instead of using one rule for everything, your brain works like a group of local, exact changes—each type of learning shaped by the signal, situation, and experience. Understanding this adds depth to memory and brain function, changing how we define intelligence, emotion, and behavior.
Final Thought: The Future of Mind, Memory, and Machine
This isn’t just a new view of neurons. It’s a big change across fields: guiding AI design, changing education ideas, and shaping future mental health diagnoses. The better we understand how the brain learns at the small level, the more intelligently we can build machines, educate minds, and heal minds.
Understanding the inner workings of memory and brain function puts us close to major advances in technology, medicine, and thought—a place where science meets feeling.
Stay curious, and keep watching.
Read more neuroscience discoveries at www.theneurotimes.com — and subscribe to stay informed about brain science.
Citations
- Bittner, K. C., & Magee, J. C. (2024). Learning rules for dendritic synapses in the mammalian brain. Science, 384(6678), 203-208. https://doi.org/10.1126/science.ads4706
- Takeuchi, T., Duszkiewicz, A. J., & Morris, R. G. (2014). The synaptic plasticity and memory hypothesis: encoding, storage, and persistence. Philosophical Transactions of the Royal Society B: Biological Sciences, 369(1633), 20130288. https://doi.org/10.1098/rstb.2013.0175
- Duman, R. S., & Aghajanian, G. K. (2012). Synaptic dysfunction in depression: potential therapeutic targets. Science, 338(6103), 68-72. https://doi.org/10.1038/nm.4050
- Richards, B. A., et al. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22(11), 1761–1770. https://doi.org/10.1038/s41593-019-0520-2
- Grant, S. G. N. (2018). Synaptopathies: Diseases of the synaptome. Current Opinion in Neurobiology, 48, 38–45. https://doi.org/10.1016/j.conb.2017.09.006
