<p>There is a passage in <a href="http://rads.stackoverflow.com/amzn/click/B000GQLCVE">On intelligence</a> about the differences between parallel processing in human versus computers :</p>

<blockquote>
  <p>From the dawn of the industrial revolution, people have viewed the
  brain as some sort of machine. They knew there weren't gears and cogs
  in the head, but it was the best metaphor they had. Somehow
  information entered the brain and the brain-machine determined how the
  body should react. During the computer age, the brain has been viewed
  as a particular type of machine, the programmable computer. And as we
  saw in chapter 1, AI researchers have stuck with this view, arguing
  that their lack of progress is only due to how small and slow
  computers remain compared to the human brain. Today's computers may be
  equivalent only to a cockroach brain, they say, but when we make
  bigger and faster computers they will be as intelligent as humans.</p>
  
  <p>There is a largely ignored problem with this brain-as-computer
  analogy. Neurons are quite slow compared to the transistors in a
  computer. A neuron collects inputs from its synapses, and combines
  these inputs together to decide when to output a spike to other
  neurons. A typical neuron can do this and reset itself in about five
  milliseconds (5 ms), or around two hundred times per second. This may
  seem fast, but a modern silicon-based computer can do one billion
  operations in a second. This means a basic computer operation is five
  million times faster than the basic operation in your brain! That is a
  very, very big difference. So how is it possible that a brain could be
  faster and more powerful than our fastest digital computers? "No
  problem," say the brain-as-computer people. "The brain is a parallel
  computer. It has billions of cells all computing at the same time.
  This parallelism vastly multiplies the processing power of the
  biological brain." </p>
  
  <p>I always felt this argument was a fallacy, and a simple thought
  experiment shows why. It is called the "one hundred–step rule." A
  human can perform significant tasks in much less time than a second.
  For example, I could show you a photograph and ask you to determine if
  there is cat in the image. Your job would be to push a button if there
  is a cat, but not if you see a bear or a warthog or a turnip. This
  task is difficult or impossible for a computer to perform today, yet a
  human can do it reliably in half a second or less. But neurons are
  slow, so in that half a second, the information entering your brain
  can only traverse a chain one hundred neurons long. That is, the brain
  "computes" solutions to problems like this in one hundred steps or
  fewer, regardless of how many total neurons might be  involved. From
  the time light enters your eye to the time you press the button, a
  chain no longer than one hundred neurons could be involved. A digital
  computer attempting to solve the same problem would take billions of
  steps. One hundred computer instructions are barely enough to move a
  single character on the computer's display, let alone do something
  interesting.</p>
  
  <p>But if I have many millions of neurons working together, isn't that
  like a parallel computer? Not really. Brains operate in parallel and
  parallel computers operate in parallel, but that's the only thing they
  have in common. Parallel computers combine many fast computers to work
  on large problems such as computing tomorrow's weather. To predict the
  weather you have to compute the physical conditions at many points on
  the planet. Each computer can work on a different location at the same
  time. But even though there may be hundreds or even thousands of
  computers working in parallel, the individual computers still need to
  perform billions or trillions of steps to accomplish their task. The
  largest conceivable parallel computer can't do anything useful in one
  hundred steps, no matter how large or how fast.</p>
  
  <p>Here is an analogy. Suppose I ask you to carry one hundred stone
  blocks across a desert. You can carry one stone at a time and it takes
  a million steps to cross the desert. You figure this will take a long
  time to complete by yourself, so you recruit a hundred workers to do
  it in parallel. The task now goes a hundred times faster, but it still
  requires a minimum of a million steps to cross the desert. Hiring more
  workers— even a thousand workers— wouldn't provide any additional
  gain. No matter how many workers you hire, the problem cannot be
  solved in less time than it takes to walk a million steps. The same is
  true for parallel computers. After a point, adding more processors
  doesn't make a difference. A computer, no matter how many processors
  it might have and no matter how fast it runs, cannot "compute" the
  answer to difficult problems in one hundred steps.</p>
  
  <p>So how can a brain perform difficult tasks in one hundred steps that
  the largest parallel computer imaginable can't solve in a million or a
  billion steps? The answer is the brain doesn't "compute" the answers
  to problems; it retrieves the answers from memory. In essence, the
  answers were stored in memory a long time ago. It only takes a few
  steps to retrieve something from memory. Slow neurons are not only
  fast enough to do this, but they constitute the memory themselves. The
  entire cortex is a memory system. It isn't a computer at all.</p>
</blockquote>

<p>The point made here is that the computing paradigm (that is, the way the whole thing works) of the brain and the computer are completely different. The computer is a <a href="http://en.wikipedia.org/wiki/Turing_machine">Turing machine</a>, and the brain is something else, possibly a memory system if you think that Jeff Hawking is right. Whatever it is, the brain is not a Turing machine.</p>

<p>To go back to your question:</p>

<blockquote>
  <p>Why can't human brains be used to do massive parallel processing in
  the same way computers are doing today?</p>
</blockquote>

<p>It has to do with the way the human brain works. If you assume that the brain will do any task in a parallel fashion, and the more neurons involved, the better the performance; then in order to maximize your performance you should use your whole brain. 1 task: 100% performance, 2 tasks: 50% performance, 3 tasks: 33% performance, and so on.</p>

<p>But if you add an "<a href="http://www.youtube.com/watch?v=yr8W8ejX6To">attention switching cost</a>" to go from one task to another, then you are better off just focusing on one task where the switching cost is zero.</p>

<p>So you <em>can</em> multitask, but it won't be efficient.</p>

CC BY-SA 4.0
2019-02-16T03:48:50.893Z
I really liked the passage. The idea is very interesting, but I am worried that it may be taking the Brain - Computer analogy [too far](https://scienceblogs.com/developingintelligence/2007/03/27/why-the-brain-is-not-like-a-co). We still don't understand **how** the brain computes solutions, it's not just about the neuron firing or not, it's also about the rate of firing and other factors detailed the in the link^.
Aditya P
CC BY-SA 4.0
2020-12-12T03:36:30.013Z
If our brain is a memory system, why is people with the strongest memory isn't the one with the highest IQ?
user4951
CC BY-SA 3.0
2012-12-25T01:09:23.203Z
2012-12-25T01:09:23.203Z
Benjamin Crouzier
<p>Maybe I just don't get it, but I see your question as confusing because:</p>

<p>1/ Your brain is capable of running multiple parallel processes. Actualy each one of tasks you've mentioned consists of number of processes that are done at the same time. Lots of your neurons and neuronal networks are being used at the very same moment.</p>

<p>2/ If you can do something automatically, without thinking about it, but not without using our brain to guide the action (there are very few things you can do without actually using your brain and its neurons), you will find that you actually are able to do even more complex tasks in parallel. Even the two you've mentioned.</p>

<p>3/ So the problem of you not being able to do some tasks together is a problem of you not being able to concentrate at both of them as good, as you do with one task. 
Your question asks for the difference using a metaphor and comparison of the 'hardware' of your brain, but the problem you are asking about goes on higher cognition. You not being able to effectively 'double' your attention, you having limited working memory capacity. </p>

CC BY-SA 3.0
2012-12-28T07:01:33.307Z
Thanks for observations you made. "...you having limited working memory capacity. " any reference on this, and  that this is the reason "...I am not able to effectively 'double' my attention"?
gpuguy
CC BY-SA 3.0
2012-12-27T11:37:11.893Z
2012-12-27T11:37:11.893Z
Oriesok Vlassky
<p>I think you are beginning to push at the limits of human brain versus computer metaphor.(<a href="http://www.joelonsoftware.com/articles/LeakyAbstractions.html" rel="nofollow">mildly related link here</a>) I'll list the objections as:</p>

<ol>
<li><p>While neurons firing can be translated/compared to 0 or 1 states, am
not convinced it is a valid equivalence. (i.e: simulating 100
billion neurons on a computer would still not be able to match a
human brain). My suspicions are while we have gone through a very
accelerated evolution in building and controlling the  performance
of transistors, evolution hasn't progressed to that stage in terms
of brain development. </p></li>
<li><p>Our neuronal cells do exhibit variations(range of electrical
activity, not just spikes and flats) and in some sense you can call
procedural learnt tasks to be parallel processing. i.e: you can
speak and drive to some extent etc..Not to mention the Autonomic nervous system and it's functionalities like  breathing, heart beat etc..<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1262103" rel="nofollow">Here's</a> the paper i was referring to. My point being this can be considered as parallel
processing.</p></li>
<li><p>Another ignored factor is the resolution at which we can control/program processors to compute and co-ordinate with each other on a single problem. Parallel processing is hardly a solved problem in the programming world. </p></li>
<li><p>Yet another factor that is ignored is that if you do zoom down on the computers to the level of capacitors, they are not all binary and have some analog activities too.<a href="http://www.mikeash.com/pyblog/friday-qa-2013-10-11-why-registers-are-fast-and-ram-is-slow.html" rel="nofollow">This article</a> mentions it, though it goes on to make a different point. </p></li>
<li><p>Another relevant point is that there are algorithms that cannot be sped up by using parallel computing. For ex: iterative algorithms like <a href="http://en.wikipedia.org/wiki/Newton%27s_method" rel="nofollow">Newton's method</a>, in these cases the next computation depends on the immediately preceding computation and so has to wait till the previous computation is done.</p></li>
</ol>

<p>So to summarize, our brains do some forms of parallel processing already.It may just be that the jobs of writing and solving geometry problem have a set of processes too much in common that they cannot be done parallel.</p>

CC BY-SA 3.0
2012-11-30T10:17:36.027Z
Nice of you to point out the brain actually does multitask, of which breathing and controlling muscles are very good examples. You make some interesting comments, but I can't up vote this as an answer.
Steven Jeuris
CC BY-SA 3.0
2012-11-30T10:37:36.370Z
I know, i initially wrote the whole thing up as a comment, but exceeded word limit. will perhaps come back when i have time to search and add proper citations.
Software Mechanic
CC BY-SA 3.0
2012-11-30T11:30:30.467Z
`Our neuronal cells do exhibit variations(range of electrical activity, not just spikes and flats)` this is an interesting point, as [graded potentials](http://en.wikipedia.org/wiki/Membrane_potential#Graded_potentials) are important in smaller organisms like insects, I don't think it's known what their role in humans is, yet, though.
Chuck Sherrington
CC BY-SA 3.0
2012-11-30T11:32:25.863Z
I agree 100% with your underlying thesis (that the brain/computer analogy becomes ever more strained as we learn more). I'm with Steven, though, in that you just need to flesh out the points a little bit.
Chuck Sherrington
CC BY-SA 3.0
2012-12-04T06:15:48.787Z
Chuck, graded potentials would be important in gap-junction coupling, probably partiuclarly axo-axonic gap junction coupling (which occurs in the dentate gyrus of the hippocampus).  They're mostly known for their role in network synchronization.

But also, underlying membrane potential is always fluxuation due to passive currents both in and outside the cell; thresholds are kind of a fuzzy value that can be altered by global activity in the body.
Keegan Keplinger
CC BY-SA 3.0
2012-11-30T07:43:39.653Z
2013-10-14T05:13:41.330Z
2013-10-14T05:13:41.330Z
Software Mechanic
<p>This probably won't be a very good answer, but I studied this a bit in a Human Factors class I took this semester. The metaphor that the professor used is a bucket of mental resources. We have several buckets for certain things. When we are using resources from the bucket the bucket has less resources for other things.</p>

<p>We can walk and talk at the same time because they are using different buckets( motor-physical and language processing ). However we are very bad at reading and talking to someone at the same time because both task are taking up mental resources from the same bucket( language processing ). </p>

<p>So, yes we are able to do various things well that aren't requiring resources from the same bucket, but these buckets are only so deep. </p>

<p>Maybe that makes since. Oh yeah there's a more technical term, maybe Multi-Channel Human Information Processing. I know if you researched Human Information Processing( HIP ) you would stumble upon what you may be looking for.</p>

CC BY-SA 3.0
2012-12-10T22:32:57.043Z
2012-12-10T22:32:57.043Z
Joey Green
<p>As computers have a different architecture, you are all just comparing an Apple with a Pie, or a Melon with a Pineapple. How can such a comparison ever be correct?
I have never seen any fruit tasting the same, and you all think the human brain(whereof we believe is conscious) is comparable with a computer whereof we don't even know if it is conscious after all. I am not a computer, so how will I ever know for sure a computer is conscious. How do I even know the people around me are conscious? I try to say, that you cannot only rely on a observation that's just a assumption of reality!
That's why the holy bible said:
Don't judge!
And just to clarify,
I do not deny any religion, neither do I try to prove it's legitimacy.
Grow up please,
and leave unanswerable questions out of the question.
The get back to what we do know.</p>

<p>Humans can give input to a computer as we assume. Computers can't give input to humans, as far as we assume.
Humans can decide as far as we assume, computers can't as far as we assume.</p>

<p>What's the specific problem in this case?</p>

<p>If I do just walk through a "Don't walk sign", I might get troubles!
If a computer walks through a "Don't walk sign", it asks us what to do.
Which decision is faster?</p>

<p>When calculating what to do next, a "Don't walk sign" involves abstract thinking, which is almost impossible to computers. Computers can do commands very easily, humans simply can't follow up commands easily. What do you think that happens if humans have a faster brain, but simply review their decision many times before making a decision, talking about emotions and fears and other stuff. We have to keep track of much more parameters, of which computers cannot even be aware of. Have you ever seen a small kid of about five years old, keeping track of all the parameters adults have to be aware of? Guess why adults learn slowly!
Our brain isn't just placing ones and zeros on a harddrive!
We are a living organism, and we cannot just place information somewhere.
Our brain is continuously fragmenting, just like computers, but humans are defragmenting all of the time, where computers start do have the ability to perform such a task simultaneously to it's calculations. But humans are basically performing a few hundreds, maybe even a few thousands of brain tasks before we even do something. Have you ever seen computers keeping track of these parameters? Till then, it's just comparing a cockroach to a human.
Does a cockroach know?</p>

CC BY-SA 3.0
2014-12-18T05:40:40.833Z
The parallel between computers and the human brain is not only very interesting, it is relevant. Trying to solve, e.g., face recognition by computer programs has taught us a lot on what the brain needs to do and which problems and bottle necks it faces. Your answer is actually more of an opinion and is better suited as a comment.
AliceD
CC BY-SA 3.0
2014-12-18T05:25:44.243Z
2014-12-18T05:25:44.243Z
J Wyckmans
<p>First of all I should say that I'm not a neuroscientist so take my answer with a grain of salt. Your first question was "Why can't human brains be used to do massive parallel processing in the same way computers are doing today?" </p>

<p>I think the answer to this question has to do with the difference between the multi-core processors computers have and the nuro networks that exist in the human brain. Most processors on a regular computer, with the exception of the GPU, are general purpose processors so that any program or algorithm can be run on any core of the computer. </p>

<p>The human brain on the other hand has specific areas of the brain specializing in processing different kinds of input. </p>

<p>In the human brain the part of the human brain which controls higher cognitive function is called the prefrontal cortex and one of the jobs of the prefrontal cortex is to determine what from other parts of the brain to listen to and what parts of the brain should be ignored. </p>

<p>Also I question your assertion that the human brain does not in fact already engage in parallel processing. Anybody able to hold a conversation while walking or driving is engaging in parallel processing. This is possible because the areas of the brain engaged in language processing are different than the areas associated with image processing and movement. </p>

CC BY-SA 3.0
2014-09-22T12:15:18.553Z
Welcome to cogsci.SE!  We encourage answers that are based in science and not merely opinion or philosophy, so some citations would go a long way towards firming up this answer--as well as giving interested parties further reading.
Krysta
CC BY-SA 3.0
2014-09-18T20:25:50.003Z
2014-09-18T20:25:50.003Z
Aaron Levenstein
<p>I am asking a question regarding parallel processing as done by billions of Neurons inside our brain and parallel processing done by our computers in a cluster for example or even on a Graphics processor having 500 processing cores.</p>

<p>Recently we have seen great success in applying computers to solve complex problems applying parallel processing, such as in the field of scientific simulations, modeling, video/image processing etc.  Also due to limitations on semiconductor devices, chip makers are now forced to develop multi-core CPUs. This encourages and motivates software developers to apply parallel programming to gain the required performance, exploiting all the processing cores inside the CPU. </p>

<p>While people are encouraging to solve problems using parallel programming on the computers, at the same time generally discouraging to do parallel processing inside the brain. Expert say that we must concentrate in one dimension to get best results. Why can't human brains be used to do massive parallel processing in the same way computers are doing today?
The brain is having billions of computational units called Neurons, and thus it provides an opportunity to do processing at both coarser level and at finer level. 
Why can't we use our brain to do certain jobs in parallel in the same way as computers, for example, while I am writing this question using my right hand, why cant I solve a problem in geometry at the same time, using my left hand? Using much less powerful computers having just two processing cores (vs billions in brain) I can do these two jobs of writing and solving geometry problem, in parallel by launching two separate threads. Why it is so?  </p>

CC BY-SA 3.0
2012-11-30T10:07:57.070Z
One thing I can answer: comparing a neuron to a computational unit seems wrong to me. Rather a set of neurons could make up a computational unit to some degree.
Steven Jeuris
CC BY-SA 3.0
2012-11-30T10:14:51.667Z
It might also be interesting to look into some papers discussing multitasking, I'm quite certain plenty of research has been done related to that.
Steven Jeuris
CC BY-SA 3.0
2012-12-11T19:07:09.010Z
A fantastic paper about the nature of the universe and how we think of it as a "computer" but probably shouldn't - http://arxiv.org/abs/1211.7081 As a graduate student also working in neuroscience, I found its overall message equally applicable to the brain
Diego
CC BY-SA 3.0
2012-12-14T11:34:57.437Z
I would like to add the idea that even computers can not break down some tasks to multiple threads. Tasks which are made up of many individual calculations can be broken down to be operated on by many processors. Rendering, filtering, modeling are all amenable to multi core processing. Individual calculations can't be broken down, so a process that consists of many calculations serially can't be broken down. One of the things that can only occupy one thread is communicating with the user.
timquinn
CC BY-SA 3.0
2012-12-14T11:34:57.437Z
This would suggest the bottleneck is the fact that you have only one consciousness. The brain does use multi-threading, I assume, when image processing or modeling the future, but we only get the results posted to the conscious mind as images or emotions. The conscious mind can only do one thing at a time and our sense of ourselves is the sum of those things we are conscious of being in control of.
timquinn
<theoretical-neuroscience><neural-network>
Difference between parallel processing done by human brain and by computers
gpuguy
# Difference between parallel processing done by human brain and by computers

(Asked by: username_0 on 2012-11-30)

I am asking a question regarding parallel processing as done by billions of Neurons inside our brain and parallel processing done by our computers in a cluster for example or even on a Graphics processor having 500 processing cores.

Recently we have seen great success in applying computers to solve complex problems applying parallel processing, such as in the field of scientific simulations, modeling, video/image processing etc. Also due to limitations on semiconductor devices, chip makers are now forced to develop multi-core CPUs. This encourages and motivates software developers to apply parallel programming to gain the required performance, exploiting all the processing cores inside the CPU.

While people are encouraging to solve problems using parallel programming on the computers, at the same time generally discouraging to do parallel processing inside the brain. Expert say that we must concentrate in one dimension to get best results. Why can't human brains be used to do massive parallel processing in the same way computers are doing today?
The brain is having billions of computational units called Neurons, and thus it provides an opportunity to do processing at both coarser level and at finer level.
Why can't we use our brain to do certain jobs in parallel in the same way as computers, for example, while I am writing this question using my right hand, why cant I solve a problem in geometry at the same time, using my left hand? Using much less powerful computers having just two processing cores (vs billions in brain) I can do these two jobs of writing and solving geometry problem, in parallel by launching two separate threads. Why it is so?

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

> **Comments:**
> **username_1**: One thing I can answer: comparing a neuron to a computational unit seems wrong to me. Rather a set of neurons could make up a computational unit to some degree.  
> **username_1**: It might also be interesting to look into some papers discussing multitasking, I'm quite certain plenty of research has been done related to that.  
> **username_2**: A fantastic paper about the nature of the universe and how we think of it as a "computer" but probably shouldn't - http://arxiv.org/abs/1211.7081 As a graduate student also working in neuroscience, I found its overall message equally applicable to the brain  
> **username_3**: I would like to add the idea that even computers can not break down some tasks to multiple threads. Tasks which are made up of many individual calculations can be broken down to be operated on by many processors. Rendering, filtering, modeling are all amenable to multi core processing. Individual calculations can't be broken down, so a process that consists of many calculations serially can't be broken down. One of the things that can only occupy one thread is communicating with the user.  
> **username_3**: This would suggest the bottleneck is the fact that you have only one consciousness. The brain does use multi-threading, I assume, when image processing or modeling the future, but we only get the results posted to the conscious mind as images or emotions. The conscious mind can only do one thing at a time and our sense of ourselves is the sum of those things we are conscious of being in control of.  

---

## 6 answers

### Answer by username_4 on 2012-12-25. Score: 11

There is a passage in [On intelligence](http://rads.stackoverflow.com/amzn/click/B000GQLCVE) about the differences between parallel processing in human versus computers :

> From the dawn of the industrial revolution, people have viewed the
> brain as some sort of machine. They knew there weren't gears and cogs
> in the head, but it was the best metaphor they had. Somehow
> information entered the brain and the brain-machine determined how the
> body should react. During the computer age, the brain has been viewed
> as a particular type of machine, the programmable computer. And as we
> saw in chapter 1, AI researchers have stuck with this view, arguing
> that their lack of progress is only due to how small and slow
> computers remain compared to the human brain. Today's computers may be
> equivalent only to a cockroach brain, they say, but when we make
> bigger and faster computers they will be as intelligent as humans.
>
> There is a largely ignored problem with this brain-as-computer
> analogy. Neurons are quite slow compared to the transistors in a
> computer. A neuron collects inputs from its synapses, and combines
> these inputs together to decide when to output a spike to other
> neurons. A typical neuron can do this and reset itself in about five
> milliseconds (5 ms), or around two hundred times per second. This may
> seem fast, but a modern silicon-based computer can do one billion
> operations in a second. This means a basic computer operation is five
> million times faster than the basic operation in your brain! That is a
> very, very big difference. So how is it possible that a brain could be
> faster and more powerful than our fastest digital computers? "No
> problem," say the brain-as-computer people. "The brain is a parallel
> computer. It has billions of cells all computing at the same time.
> This parallelism vastly multiplies the processing power of the
> biological brain."
>
> I always felt this argument was a fallacy, and a simple thought
> experiment shows why. It is called the "one hundred–step rule." A
> human can perform significant tasks in much less time than a second.
> For example, I could show you a photograph and ask you to determine if
> there is cat in the image. Your job would be to push a button if there
> is a cat, but not if you see a bear or a warthog or a turnip. This
> task is difficult or impossible for a computer to perform today, yet a
> human can do it reliably in half a second or less. But neurons are
> slow, so in that half a second, the information entering your brain
> can only traverse a chain one hundred neurons long. That is, the brain
> "computes" solutions to problems like this in one hundred steps or
> fewer, regardless of how many total neurons might be involved. From
> the time light enters your eye to the time you press the button, a
> chain no longer than one hundred neurons could be involved. A digital
> computer attempting to solve the same problem would take billions of
> steps. One hundred computer instructions are barely enough to move a
> single character on the computer's display, let alone do something
> interesting.
>
> But if I have many millions of neurons working together, isn't that
> like a parallel computer? Not really. Brains operate in parallel and
> parallel computers operate in parallel, but that's the only thing they
> have in common. Parallel computers combine many fast computers to work
> on large problems such as computing tomorrow's weather. To predict the
> weather you have to compute the physical conditions at many points on
> the planet. Each computer can work on a different location at the same
> time. But even though there may be hundreds or even thousands of
> computers working in parallel, the individual computers still need to
> perform billions or trillions of steps to accomplish their task. The
> largest conceivable parallel computer can't do anything useful in one
> hundred steps, no matter how large or how fast.
>
> Here is an analogy. Suppose I ask you to carry one hundred stone
> blocks across a desert. You can carry one stone at a time and it takes
> a million steps to cross the desert. You figure this will take a long
> time to complete by yourself, so you recruit a hundred workers to do
> it in parallel. The task now goes a hundred times faster, but it still
> requires a minimum of a million steps to cross the desert. Hiring more
> workers— even a thousand workers— wouldn't provide any additional
> gain. No matter how many workers you hire, the problem cannot be
> solved in less time than it takes to walk a million steps. The same is
> true for parallel computers. After a point, adding more processors
> doesn't make a difference. A computer, no matter how many processors
> it might have and no matter how fast it runs, cannot "compute" the
> answer to difficult problems in one hundred steps.
>
> So how can a brain perform difficult tasks in one hundred steps that
> the largest parallel computer imaginable can't solve in a million or a
> billion steps? The answer is the brain doesn't "compute" the answers
> to problems; it retrieves the answers from memory. In essence, the
> answers were stored in memory a long time ago. It only takes a few
> steps to retrieve something from memory. Slow neurons are not only
> fast enough to do this, but they constitute the memory themselves. The
> entire cortex is a memory system. It isn't a computer at all.

The point made here is that the computing paradigm (that is, the way the whole thing works) of the brain and the computer are completely different. The computer is a [Turing machine](http://en.wikipedia.org/wiki/Turing_machine), and the brain is something else, possibly a memory system if you think that Jeff Hawking is right. Whatever it is, the brain is not a Turing machine.

To go back to your question:

> Why can't human brains be used to do massive parallel processing in
> the same way computers are doing today?

It has to do with the way the human brain works. If you assume that the brain will do any task in a parallel fashion, and the more neurons involved, the better the performance; then in order to maximize your performance you should use your whole brain. 1 task: 100% performance, 2 tasks: 50% performance, 3 tasks: 33% performance, and so on.

But if you add an "[attention switching cost](http://www.youtube.com/watch?v=yr8W8ejX6To)" to go from one task to another, then you are better off just focusing on one task where the switching cost is zero.

So you *can* multitask, but it won't be efficient.

> **Comments:**
> **username_5**: I really liked the passage. The idea is very interesting, but I am worried that it may be taking the Brain - Computer analogy [too far](https://scienceblogs.com/developingintelligence/2007/03/27/why-the-brain-is-not-like-a-co). We still don't understand **how** the brain computes solutions, it's not just about the neuron firing or not, it's also about the rate of firing and other factors detailed the in the link^.  
> **username_6**: If our brain is a memory system, why is people with the strongest memory isn't the one with the highest IQ?  

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### Answer by username_7 on 2012-12-27. Score: 5

Maybe I just don't get it, but I see your question as confusing because:

1/ Your brain is capable of running multiple parallel processes. Actualy each one of tasks you've mentioned consists of number of processes that are done at the same time. Lots of your neurons and neuronal networks are being used at the very same moment.

2/ If you can do something automatically, without thinking about it, but not without using our brain to guide the action (there are very few things you can do without actually using your brain and its neurons), you will find that you actually are able to do even more complex tasks in parallel. Even the two you've mentioned.

3/ So the problem of you not being able to do some tasks together is a problem of you not being able to concentrate at both of them as good, as you do with one task.
Your question asks for the difference using a metaphor and comparison of the 'hardware' of your brain, but the problem you are asking about goes on higher cognition. You not being able to effectively 'double' your attention, you having limited working memory capacity.

> **Comments:**
> **username_0**: Thanks for observations you made. "...you having limited working memory capacity. " any reference on this, and  that this is the reason "...I am not able to effectively 'double' my attention"?  

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### Answer by username_8 on 2012-11-30. Score: 4

I think you are beginning to push at the limits of human brain versus computer metaphor.([mildly related link here](http://www.joelonsoftware.com/articles/LeakyAbstractions.html)) I'll list the objections as:

1. While neurons firing can be translated/compared to 0 or 1 states, am
   not convinced it is a valid equivalence. (i.e: simulating 100
   billion neurons on a computer would still not be able to match a
   human brain). My suspicions are while we have gone through a very
   accelerated evolution in building and controlling the performance
   of transistors, evolution hasn't progressed to that stage in terms
   of brain development.
2. Our neuronal cells do exhibit variations(range of electrical
   activity, not just spikes and flats) and in some sense you can call
   procedural learnt tasks to be parallel processing. i.e: you can
   speak and drive to some extent etc..Not to mention the Autonomic nervous system and it's functionalities like breathing, heart beat etc..[Here's](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1262103) the paper i was referring to. My point being this can be considered as parallel
   processing.
3. Another ignored factor is the resolution at which we can control/program processors to compute and co-ordinate with each other on a single problem. Parallel processing is hardly a solved problem in the programming world.
4. Yet another factor that is ignored is that if you do zoom down on the computers to the level of capacitors, they are not all binary and have some analog activities too.[This article](http://www.mikeash.com/pyblog/friday-qa-2013-10-11-why-registers-are-fast-and-ram-is-slow.html) mentions it, though it goes on to make a different point.
5. Another relevant point is that there are algorithms that cannot be sped up by using parallel computing. For ex: iterative algorithms like [Newton's method](http://en.wikipedia.org/wiki/Newton%27s_method), in these cases the next computation depends on the immediately preceding computation and so has to wait till the previous computation is done.

So to summarize, our brains do some forms of parallel processing already.It may just be that the jobs of writing and solving geometry problem have a set of processes too much in common that they cannot be done parallel.

> **Comments:**
> **username_1**: Nice of you to point out the brain actually does multitask, of which breathing and controlling muscles are very good examples. You make some interesting comments, but I can't up vote this as an answer.  
> **username_8**: I know, i initially wrote the whole thing up as a comment, but exceeded word limit. will perhaps come back when i have time to search and add proper citations.  
> **username_9**: `Our neuronal cells do exhibit variations(range of electrical activity, not just spikes and flats)` this is an interesting point, as [graded potentials](http://en.wikipedia.org/wiki/Membrane_potential#Graded_potentials) are important in smaller organisms like insects, I don't think it's known what their role in humans is, yet, though.  
> **username_9**: I agree 100% with your underlying thesis (that the brain/computer analogy becomes ever more strained as we learn more). I'm with Steven, though, in that you just need to flesh out the points a little bit.  
> **username_10**: Chuck, graded potentials would be important in gap-junction coupling, probably partiuclarly axo-axonic gap junction coupling (which occurs in the dentate gyrus of the hippocampus).  They're mostly known for their role in network synchronization.  But also, underlying membrane potential is always fluxuation due to passive currents both in and outside the cell; thresholds are kind of a fuzzy value that can be altered by global activity in the body.  

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### Answer by username_11 on 2012-12-10. Score: 1

This probably won't be a very good answer, but I studied this a bit in a Human Factors class I took this semester. The metaphor that the professor used is a bucket of mental resources. We have several buckets for certain things. When we are using resources from the bucket the bucket has less resources for other things.

We can walk and talk at the same time because they are using different buckets( motor-physical and language processing ). However we are very bad at reading and talking to someone at the same time because both task are taking up mental resources from the same bucket( language processing ).

So, yes we are able to do various things well that aren't requiring resources from the same bucket, but these buckets are only so deep.

Maybe that makes since. Oh yeah there's a more technical term, maybe Multi-Channel Human Information Processing. I know if you researched Human Information Processing( HIP ) you would stumble upon what you may be looking for.


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### Answer by username_12 on 2014-12-18. Score: 0

As computers have a different architecture, you are all just comparing an Apple with a Pie, or a Melon with a Pineapple. How can such a comparison ever be correct?
I have never seen any fruit tasting the same, and you all think the human brain(whereof we believe is conscious) is comparable with a computer whereof we don't even know if it is conscious after all. I am not a computer, so how will I ever know for sure a computer is conscious. How do I even know the people around me are conscious? I try to say, that you cannot only rely on a observation that's just a assumption of reality!
That's why the holy bible said:
Don't judge!
And just to clarify,
I do not deny any religion, neither do I try to prove it's legitimacy.
Grow up please,
and leave unanswerable questions out of the question.
The get back to what we do know.

Humans can give input to a computer as we assume. Computers can't give input to humans, as far as we assume.
Humans can decide as far as we assume, computers can't as far as we assume.

What's the specific problem in this case?

If I do just walk through a "Don't walk sign", I might get troubles!
If a computer walks through a "Don't walk sign", it asks us what to do.
Which decision is faster?

When calculating what to do next, a "Don't walk sign" involves abstract thinking, which is almost impossible to computers. Computers can do commands very easily, humans simply can't follow up commands easily. What do you think that happens if humans have a faster brain, but simply review their decision many times before making a decision, talking about emotions and fears and other stuff. We have to keep track of much more parameters, of which computers cannot even be aware of. Have you ever seen a small kid of about five years old, keeping track of all the parameters adults have to be aware of? Guess why adults learn slowly!
Our brain isn't just placing ones and zeros on a harddrive!
We are a living organism, and we cannot just place information somewhere.
Our brain is continuously fragmenting, just like computers, but humans are defragmenting all of the time, where computers start do have the ability to perform such a task simultaneously to it's calculations. But humans are basically performing a few hundreds, maybe even a few thousands of brain tasks before we even do something. Have you ever seen computers keeping track of these parameters? Till then, it's just comparing a cockroach to a human.
Does a cockroach know?

> **Comments:**
> **username_13**: The parallel between computers and the human brain is not only very interesting, it is relevant. Trying to solve, e.g., face recognition by computer programs has taught us a lot on what the brain needs to do and which problems and bottle necks it faces. Your answer is actually more of an opinion and is better suited as a comment.  

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### Answer by username_14 on 2014-09-18. Score: 0

First of all I should say that I'm not a neuroscientist so take my answer with a grain of salt. Your first question was "Why can't human brains be used to do massive parallel processing in the same way computers are doing today?"

I think the answer to this question has to do with the difference between the multi-core processors computers have and the nuro networks that exist in the human brain. Most processors on a regular computer, with the exception of the GPU, are general purpose processors so that any program or algorithm can be run on any core of the computer.

The human brain on the other hand has specific areas of the brain specializing in processing different kinds of input.

In the human brain the part of the human brain which controls higher cognitive function is called the prefrontal cortex and one of the jobs of the prefrontal cortex is to determine what from other parts of the brain to listen to and what parts of the brain should be ignored.

Also I question your assertion that the human brain does not in fact already engage in parallel processing. Anybody able to hold a conversation while walking or driving is engaging in parallel processing. This is possible because the areas of the brain engaged in language processing are different than the areas associated with image processing and movement.

> **Comments:**
> **username_15**: Welcome to cogsci.SE!  We encourage answers that are based in science and not merely opinion or philosophy, so some citations would go a long way towards firming up this answer--as well as giving interested parties further reading.  

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