<p>It is correct that only a small percentage of neurons increase their activities  relative to their baseline level in response to   new stimuli or to more abstract thoughts. There are many computational advantages to this. For instance, in deep learning, sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. Basically you have more combinations to store patterns if you activate a few cells than if you either activate too few or too many. </p>

CC BY-SA 3.0
2015-11-10T00:21:46.513Z
2015-11-10T00:21:46.513Z
Wolphram jonny
<p>It is well known that the common myth that an individual  only ever uses 10% of their brain is.. well, a myth. I had a question about a possible interpretation of this idea, and a follow-up question about the result of that interpretation. </p>

<p>After spending a small amount of time with computational neural networks, it seems that at any given time, a small percentage of neurons are actually firing. All neurons have the potential to be firing, and at some point in execution likely will fire, but due to efficiency, only very few are needed for a particular function. This is my interpretation of what I have seen so far.. is this interpretation valid for the activity of neurons in the brain?</p>

<p>If this is valid, what would happen if 90-100% of neurons were firing at all times? I'm assuming this would be a huge disadvantage to whoever this happened to. Epilepsy? Overheating? Would the body be overworked and go into shock?</p>

CC BY-SA 3.0
2015-09-30T18:39:58.290Z
All neurons in your brain fire all the time. Most of this firework, let's say 90%, is used to maintain basic functions (you, your brain, control over your body). 
The 10% rest can by used to think about something, e.g. your brain. But I'm not an expert...
draks ...
CC BY-SA 3.0
2015-09-30T19:30:09.347Z
Hmm.. so then by rerouting all of the neurons to thought, all of your involuntary functions would stop? So you would need to be hooked up to a breathing machine, IV because you couldnt digest your food, etc?
Kevin Kesicki
CC BY-SA 3.0
2015-09-30T20:08:15.140Z
This won't work, but if, then I'd bet yes. Maybe you're interested in this: I once got an answer on my question "How many calories do we burn when we try to understand mathematical proofs?". The short answer was: [Not many](http://cogsci.stackexchange.com/a/5330/370)...
draks ...
CC BY-SA 3.0
2017-09-04T15:29:49.357Z
The comparison with neural networks is awesome.  However, in the implementations of neural networks that I work on - I leverage feedback loops, and don't avoid feedback loops (like the Human brain) - I merely lessen the intensity of feedback loops.  Your analogy does seem apropos to what I do.  I am honestly not sure if any System, (even quantum ones), could "full throttle" a feedback loop, regardless the processing power, (it could probably make it worse the more processing power there was).  I can't speak to neurons, but *are you asking in terms of neurology or neural networks?
elika kohen
<neuroscience><theoretical-neuroscience><neural-network>
Interpretation & Actual Result of "10% of your brain" Myth
Kevin Kesicki
# Interpretation & Actual Result of "10% of your brain" Myth

(Asked by: username_0 on 2015-09-30)

It is well known that the common myth that an individual only ever uses 10% of their brain is.. well, a myth. I had a question about a possible interpretation of this idea, and a follow-up question about the result of that interpretation.

After spending a small amount of time with computational neural networks, it seems that at any given time, a small percentage of neurons are actually firing. All neurons have the potential to be firing, and at some point in execution likely will fire, but due to efficiency, only very few are needed for a particular function. This is my interpretation of what I have seen so far.. is this interpretation valid for the activity of neurons in the brain?

If this is valid, what would happen if 90-100% of neurons were firing at all times? I'm assuming this would be a huge disadvantage to whoever this happened to. Epilepsy? Overheating? Would the body be overworked and go into shock?

**Tags:** neuroscience, theoretical-neuroscience, neural-network

> **Comments:**
> **username_1**: All neurons in your brain fire all the time. Most of this firework, let's say 90%, is used to maintain basic functions (you, your brain, control over your body).  The 10% rest can by used to think about something, e.g. your brain. But I'm not an expert...  
> **username_0**: Hmm.. so then by rerouting all of the neurons to thought, all of your involuntary functions would stop? So you would need to be hooked up to a breathing machine, IV because you couldnt digest your food, etc?  
> **username_1**: This won't work, but if, then I'd bet yes. Maybe you're interested in this: I once got an answer on my question "How many calories do we burn when we try to understand mathematical proofs?". The short answer was: [Not many](http://cogsci.stackexchange.com/a/5330/370)...  
> **username_2**: The comparison with neural networks is awesome.  However, in the implementations of neural networks that I work on - I leverage feedback loops, and don't avoid feedback loops (like the Human brain) - I merely lessen the intensity of feedback loops.  Your analogy does seem apropos to what I do.  I am honestly not sure if any System, (even quantum ones), could "full throttle" a feedback loop, regardless the processing power, (it could probably make it worse the more processing power there was).  I can't speak to neurons, but *are you asking in terms of neurology or neural networks?  

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## 1 answers

### Answer by username_3 on 2015-11-10. Score: -4

It is correct that only a small percentage of neurons increase their activities relative to their baseline level in response to new stimuli or to more abstract thoughts. There are many computational advantages to this. For instance, in deep learning, sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently. Basically you have more combinations to store patterns if you activate a few cells than if you either activate too few or too many.


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