<p>Learning something can be said to be a process where you have made changes to your brain to the point of now being able to produce new thoughts in response to internal thoughts or external impressions. These thoughts produced will be perceived by you and conscious ones will carry some meaning.</p>
<p>Your question asks how we are able to create and edit these pathways which provide the framework for generating these thoughts/ideas, which has been the purpose of the learning.</p>
<p>Neuron activation is the most prevalent and known activity in the brain which relates to the phenomenon of having thoughts. When a neural is activated, its dendrites and axon terminals prime for connection with other neurons, they do this by physically extending its axons out into space in an effort to encounter another brain cell reaching out for connections. In this case, both neurons are activated and this is causing them to reach out for connections. Because both neurons are reaching out, naturally, there is a higher chance that they come close to each other, are attracted to each other, and as a result, bind together, forming a connection between them. This connection now means that when one neuron is activated, the other will react in some way, which is what facilitates thought.</p>
<p>This process does not involve back propagation. It is an absolute bond which forms in real time, of which can be said to have had an absolute point in time when that connection was established, and this connection has one effect, to allow for transmission of chemicals between the connected neurons which allows for them to have an effect on each other. Back propagation involves many nodes in a neural network and allows for the editing of multiple nodes in one sweep, which is not similar to how the brain functions.</p>
<p>Researchers postulate how the brain learns without back propagation, but the brain and a computer, which a neural network runs on, are different, therefore its wrong to assume brains need back propagation to learn just because current forms of neural networks need back propagation to learn. A computer which a neural network runs on is a bunch of transistors, and gates, which perform operations for the purpose of turning transistors either on or off. The brain is a network which runs on atoms and molecules which are constantly affecting each other in a dynamic ever changing way, with other avenues for altering its structure. A neural network is a purposefully constructed network which has absolute mechanisms for altering itself which have been programmatically implemented to be its primary way of learning.</p>
<p>Ultimately, the brain does not conduct itself, and is not constructed, upon the same underlying structure as a neural network, which is what allows it to learn in ways that don't require back propagation.</p>

CC BY-SA 4.0
2024-12-16T15:03:13.537Z
Exactly, hence my question.  How the brain can learn seems to be a complete mystery.
yters
CC BY-SA 4.0
2024-12-16T12:07:12.827Z
2024-12-16T12:07:12.827Z
Mike Wazowski
<p>First some background.  The field of artificial neural networks was inspired by early brain neuron research, such as that by McCulloch and Pitts.  However, the early ANN research stalled due to the XOR problem and no effective training algorithm.  Multilayer neural networks solved the first problem, but then that led to the training problem, since a single layer perceptron could be trained with the pocket algorithm, but that was no longer possible with a multilayer perceptron.  Finally, backpropagation was discovered, and that lead to the modern ANN renaissance.</p>
<p>Now, back to the question.  From the history of ANN it seems that backpropagation is essential to make ANNs learn.  No other effective approach has been discovered.</p>
<p>At the same time, the brain does not seem to exhibit anything like backpropagation.  And even if it did, the signal propagation seems too noisy and not granular enough for backpropagation to work.</p>
<p>So, since our best attempts to replication the brain's ability to learn synthetically results in a construct that has no biological corrollary, how is it possible for the brain to learn, and extremely effectively at that?  Do we have any idea what sort of algorithm the brain uses to learn, if it can't use backpropagation?  Is it like a global metaheurastic such as simulated annealing or evolutionary algorithms?</p>

CC BY-SA 4.0
2024-12-10T18:57:01.420Z
Possible duplicate of: [How do neurons decide how to alter their output signals?](https://psychology.stackexchange.com/q/10783/7001) Also see: [Biological plausibility of RBMs](https://psychology.stackexchange.com/q/16246/7001).
Arnon Weinberg
CC BY-SA 4.0
2024-12-11T13:28:23.363Z
@ArnonWeinberg thanks, the first question seems related.  However, Hebbian learning is not a very good learning algorithm.  It seems implausible the brain uses Hebbian learning, since the brain seems to learn things that even our best neural networks cannot learn yet, and SOTA neural networks use much, much more effective algorithms than Hebbian learning.
yters
CC BY-SA 4.0
2024-12-11T20:25:57.130Z
The brain is massively parallel and recurrent, it doesn't make much sense to discuss algorithms for feedforward ANNs in the context of real brain circuitry.
Bryan Krause
CC BY-SA 4.0
2024-12-12T02:58:55.400Z
@BryanKrause Feedforward ANNs are massively parallel (see NVIDIA's stock boom), and variants are recurrent.  However, recurrency dramatically impacts the learning rate in a negative way, which is why the transformer architecture was adopted instead of recurrent neural network.  So, still it is very mysterious how the brain learns much more effectively than modern neural networks, yet modern neural networks rejected all the potential learning mechanisms we've identified in the brain as being much to inefficient to learn even in a massively parallel format.
yters
CC BY-SA 4.0
2024-12-12T03:00:55.973Z
Here's a good related question: https://psychology.stackexchange.com/questions/26883/what-is-required-reading-in-training-that-describe-mechanisms-behind-learning
yters
CC BY-SA 4.0
2024-12-12T04:04:05.763Z
Or to put it another way, wetware is very different from hardware. I'm aware of a great gulf between a programmer's understanding of AI, and a neuroscientist's understanding - AI is designed, the brain has evolved and it's activity is [emergent](https://en.wikipedia.org/wiki/Emergence). This signifies that the investigation of such things is not finished, but known to  be much more complex than a set of algorithms and logical operations. There's a whole mess of neurotransmitters and hormones at play too.
Jiminy Cricket.
CC BY-SA 4.0
2024-12-12T15:16:41.337Z
How does neuroscience define learning in a scientific way that is different than in the fields of AI and machine learning?  At any rate, it seems the takeaway here is that insofar as computer science defines learning, the brain does not do anything like that at all.
yters
CC BY-SA 4.0
2024-12-12T15:59:27.130Z
"How does neuroscience define learning [....]?" Not in a single way, it's multi-layered - all the way up from molecular levels through gene-expression changes, protein manufacture, long term potentiation, growth (and retraction) of dendrites etc.. Overall the definition would be the same - to do with laying down or modification of memory leading to behavioural change.
Jiminy Cricket.
CC BY-SA 4.0
2024-12-12T16:43:32.040Z
Does neuroscience have a theory of how the learning mechanism works?  I don't mean a listing of all the biological components of a learning system, but how the components allow the brain to go from no knowledge to some knowledge.  How are all the signals from various senses turned into concepts?  How are the concepts stored?  How are correct concepts distinguished from false concepts?  How does the brain use these concepts to make predictions about the world?  All of this is described in computer science, so I'm trying to understand how neuroscience has a different definition.
yters
CC BY-SA 4.0
2024-12-12T17:17:36.150Z
That's a lot of big questions. I can't answer, certainly not in the space of an answer here, much less in comments. The background reading listed in the link you posted as a comment would be worth a dive down the rabbit hole.
Jiminy Cricket.
<learning><brain><neural-network>
How is it possible for brain neurons to learn if they don't do backpropagation?
yters
# How is it possible for brain neurons to learn if they don't do backpropagation?

(Asked by: username_0 on 2024-12-10)

First some background. The field of artificial neural networks was inspired by early brain neuron research, such as that by McCulloch and Pitts. However, the early ANN research stalled due to the XOR problem and no effective training algorithm. Multilayer neural networks solved the first problem, but then that led to the training problem, since a single layer perceptron could be trained with the pocket algorithm, but that was no longer possible with a multilayer perceptron. Finally, backpropagation was discovered, and that lead to the modern ANN renaissance.

Now, back to the question. From the history of ANN it seems that backpropagation is essential to make ANNs learn. No other effective approach has been discovered.

At the same time, the brain does not seem to exhibit anything like backpropagation. And even if it did, the signal propagation seems too noisy and not granular enough for backpropagation to work.

So, since our best attempts to replication the brain's ability to learn synthetically results in a construct that has no biological corrollary, how is it possible for the brain to learn, and extremely effectively at that? Do we have any idea what sort of algorithm the brain uses to learn, if it can't use backpropagation? Is it like a global metaheurastic such as simulated annealing or evolutionary algorithms?

**Tags:** learning, brain, neural-network

> **Comments:**
> **username_1**: Possible duplicate of: [How do neurons decide how to alter their output signals?](https://psychology.stackexchange.com/q/10783/7001) Also see: [Biological plausibility of RBMs](https://psychology.stackexchange.com/q/16246/7001).  
> **username_0**: @ArnonWeinberg thanks, the first question seems related.  However, Hebbian learning is not a very good learning algorithm.  It seems implausible the brain uses Hebbian learning, since the brain seems to learn things that even our best neural networks cannot learn yet, and SOTA neural networks use much, much more effective algorithms than Hebbian learning.  
> **username_2**: The brain is massively parallel and recurrent, it doesn't make much sense to discuss algorithms for feedforward ANNs in the context of real brain circuitry.  
> **username_0**: @BryanKrause Feedforward ANNs are massively parallel (see NVIDIA's stock boom), and variants are recurrent.  However, recurrency dramatically impacts the learning rate in a negative way, which is why the transformer architecture was adopted instead of recurrent neural network.  So, still it is very mysterious how the brain learns much more effectively than modern neural networks, yet modern neural networks rejected all the potential learning mechanisms we've identified in the brain as being much to inefficient to learn even in a massively parallel format.  
> **username_0**: Here's a good related question: https://psychology.stackexchange.com/questions/26883/what-is-required-reading-in-training-that-describe-mechanisms-behind-learning  
> **username_3**: Or to put it another way, wetware is very different from hardware. I'm aware of a great gulf between a programmer's understanding of AI, and a neuroscientist's understanding - AI is designed, the brain has evolved and it's activity is [emergent](https://en.wikipedia.org/wiki/Emergence). This signifies that the investigation of such things is not finished, but known to  be much more complex than a set of algorithms and logical operations. There's a whole mess of neurotransmitters and hormones at play too.  
> **username_0**: How does neuroscience define learning in a scientific way that is different than in the fields of AI and machine learning?  At any rate, it seems the takeaway here is that insofar as computer science defines learning, the brain does not do anything like that at all.  
> **username_3**: "How does neuroscience define learning [....]?" Not in a single way, it's multi-layered - all the way up from molecular levels through gene-expression changes, protein manufacture, long term potentiation, growth (and retraction) of dendrites etc.. Overall the definition would be the same - to do with laying down or modification of memory leading to behavioural change.  
> **username_0**: Does neuroscience have a theory of how the learning mechanism works?  I don't mean a listing of all the biological components of a learning system, but how the components allow the brain to go from no knowledge to some knowledge.  How are all the signals from various senses turned into concepts?  How are the concepts stored?  How are correct concepts distinguished from false concepts?  How does the brain use these concepts to make predictions about the world?  All of this is described in computer science, so I'm trying to understand how neuroscience has a different definition.  
> **username_3**: That's a lot of big questions. I can't answer, certainly not in the space of an answer here, much less in comments. The background reading listed in the link you posted as a comment would be worth a dive down the rabbit hole.  

---

## 1 answers

### Answer by username_4 on 2024-12-16. Score: 2

Learning something can be said to be a process where you have made changes to your brain to the point of now being able to produce new thoughts in response to internal thoughts or external impressions. These thoughts produced will be perceived by you and conscious ones will carry some meaning.

Your question asks how we are able to create and edit these pathways which provide the framework for generating these thoughts/ideas, which has been the purpose of the learning.

Neuron activation is the most prevalent and known activity in the brain which relates to the phenomenon of having thoughts. When a neural is activated, its dendrites and axon terminals prime for connection with other neurons, they do this by physically extending its axons out into space in an effort to encounter another brain cell reaching out for connections. In this case, both neurons are activated and this is causing them to reach out for connections. Because both neurons are reaching out, naturally, there is a higher chance that they come close to each other, are attracted to each other, and as a result, bind together, forming a connection between them. This connection now means that when one neuron is activated, the other will react in some way, which is what facilitates thought.

This process does not involve back propagation. It is an absolute bond which forms in real time, of which can be said to have had an absolute point in time when that connection was established, and this connection has one effect, to allow for transmission of chemicals between the connected neurons which allows for them to have an effect on each other. Back propagation involves many nodes in a neural network and allows for the editing of multiple nodes in one sweep, which is not similar to how the brain functions.

Researchers postulate how the brain learns without back propagation, but the brain and a computer, which a neural network runs on, are different, therefore its wrong to assume brains need back propagation to learn just because current forms of neural networks need back propagation to learn. A computer which a neural network runs on is a bunch of transistors, and gates, which perform operations for the purpose of turning transistors either on or off. The brain is a network which runs on atoms and molecules which are constantly affecting each other in a dynamic ever changing way, with other avenues for altering its structure. A neural network is a purposefully constructed network which has absolute mechanisms for altering itself which have been programmatically implemented to be its primary way of learning.

Ultimately, the brain does not conduct itself, and is not constructed, upon the same underlying structure as a neural network, which is what allows it to learn in ways that don't require back propagation.

> **Comments:**
> **username_0**: Exactly, hence my question.  How the brain can learn seems to be a complete mystery.  

---

