<p>This line of thought actually represents the current state of the art in scientific approaches to the brain. Before we discovered electricity and that (peripheral) neurons conduct electricity, one analogy or explanation suggested that the mind worked through hydraulic action.</p>

<p>It is therefore possible that we have to abandon this analogy, if we find a better one. For now, we give it explanatory power. However, we do know that the brain circuitry is very different from a computer. At the same time, neurons have discrete outputs, which can be represented in binary (<a href="http://www.scholarpedia.org/article/FitzHugh-Nagumo_model#Absence_of_all-or-none_spikes" rel="nofollow noreferrer">all-or-none</a>). (But there is also non-spiking neurons with a more continuous "analog" response.)</p>

<p>Suggested readings:</p>

<p><a href="https://plato.stanford.edu/entries/cognitive-science/#RepCom" rel="nofollow noreferrer">Cognitive Science</a></p>

<p><a href="https://plato.stanford.edu/entries/computational-mind/" rel="nofollow noreferrer">Philosohpy of Mind: Computationalism</a></p>

<p><a href="http://www.scholarpedia.org/article/Chinese_room_argument" rel="nofollow noreferrer">A classical argument against computationalism: The Chinese Room Argument</a></p>

CC BY-SA 4.0
2018-07-01T08:38:50.673Z
2018-07-31T12:42:48.770Z
2018-07-31T12:42:48.770Z
noumenal
<p>This question's reference to a classical computer refers to a &quot;<a href="https://en.wikipedia.org/wiki/Turing_machine" rel="nofollow noreferrer">Turing Machine</a>&quot; style of computation, also known as a <a href="https://en.wikipedia.org/wiki/Knowledge-based_systems" rel="nofollow noreferrer">knowledge system</a>, in which decisions and possible results are pre-programmed using if-statements, loops, and other logical constructs.</p>
<p>However, most modern computer programmers and engineers are at least somewhat familiar with neural networks, used for <a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow noreferrer">machine learning</a>. <a href="https://en.wikipedia.org/wiki/Artificial_neural_network" rel="nofollow noreferrer">Artificial neural networks (ANNs)</a> are loosely based on the way the brain (a <a href="https://en.wikipedia.org/wiki/Biological_neural_network" rel="nofollow noreferrer">biological neural network</a>) works.  If you want to use your computing background to understand the brain, then I recommend learning about neural networks instead.  If you already have some understanding of neural networks, then think about how Siri or Watson work.</p>
<p>While neural networks can certainly be thought of as storing data, executing algorithms, parallel processing, and having memory and speed measurements, they are conceptually very different from classical computers, and generally uninstructive to compare. In particular, the &quot;<a href="https://en.wikipedia.org/wiki/Von_Neumann_architecture" rel="nofollow noreferrer">von Neumann</a>&quot; architectural style of modern computers does not have an analog in the brain (there is no separation between areas that compute and areas that store memories, for example). If you are still interested in such a comparison, then many of the questions listed above already have great answers, so check them out.</p>
<p>If you are interested in modeling the brain using a classical computing methodology, then check out <a href="https://en.wikipedia.org/wiki/ACT-R" rel="nofollow noreferrer">ACT-R</a>, a popular architecture for modeling the brain using a modular, knowledge-based system (also see this <a href="https://psychology.stackexchange.com/q/14064/7001">forum question</a>).</p>
<p>Artificial neural networks are not a perfect model for biological neural networks either, and if you are interested in the difference, then see this <a href="https://psychology.stackexchange.com/q/7880/7001">forum question</a>.</p>

CC BY-SA 4.0
2018-07-08T12:19:11.607Z
I doubt that the question refers to a 'Turing Machine' style of computation (i.e. infinite tape with DFA control structure). I suspect programmers are thinking in terms of the [von Neumann architecture](https://en.wikipedia.org/wiki/Von_Neumann_architecture) instead. Of course, the point of CS is that these models (including ANNs) are equivalent & many of the questions we might ask about one translate over (upto some simulation factors) to the others. I would also be skeptical of encouraging engineers to think they'll learn something about the brain (nevermind mind) from looking at CS ANNs.
Artem Kaznatcheev
CC BY-SA 4.0
2020-12-19T18:53:49.487Z
Comments are not for extended discussion; this conversation has been [moved to chat](https://chat.stackexchange.com/rooms/117459/discussion-on-answer-by-arnon-weinberg-the-computer-model-of-the-brain).
Arnon Weinberg
CC BY-SA 4.0
2018-06-17T23:41:38.760Z
2020-12-19T19:17:17.763Z
2020-12-19T19:17:17.763Z
Arnon Weinberg
<p><strong>I am a computer programmer or computer engineer, and am interested in comparing the brain to a classical computer in some way.  How well does this comparison hold up?</strong></p>

<p>This is a general introduction to the computer/brain metaphor and its practical limitations.  Similar questions on this forum include:</p>

<ul>
<li><a href="https://psychology.stackexchange.com/q/1946/7001">Difference between parallel processing done by human brain and by computers</a></li>
<li><a href="https://psychology.stackexchange.com/q/19573/7001">How does the brain generate answers to questions?</a></li>
<li><a href="https://psychology.stackexchange.com/q/8646/7001">What is the data structure used by the brain to store data?</a></li>
<li><a href="https://psychology.stackexchange.com/q/12642/7001">How is information stored in the brain</a></li>
<li><a href="https://psychology.stackexchange.com/q/19933/7001">How the Brain Does Garbage Collection in Real Time</a></li>
<li><a href="https://psychology.stackexchange.com/q/12252/7001">How does a human classify or cluster data?</a></li>
<li><a href="https://psychology.stackexchange.com/q/15478/7001">Human brain v/s computer: speed comparison?</a></li>
<li><a href="https://psychology.stackexchange.com/q/12886/7001">Does the brain have a &#39;debugging mode&#39;?</a></li>
<li><a href="https://psychology.stackexchange.com/q/8021/7001">Why can&#39;t we use 100% of the brain in a certain moment?</a></li>
<li><a href="https://psychology.stackexchange.com/q/12923/7001">Is it possible to increase the capacity of human brain like “Lucy”?</a></li>
<li><a href="https://psychology.stackexchange.com/q/12813/7001">In what ways do our brains use compression?</a></li>
<li><a href="https://psychology.stackexchange.com/q/12406/7001">How many possible states does a brain have?</a></li>
<li><a href="https://psychology.stackexchange.com/q/17463/7001">What is the capacity of the human brain?</a></li>
</ul>

CC BY-SA 4.0
2018-06-18T10:51:46.687Z
I understand what you are trying to do here, Arnon, but as is it does not seem to fit the Q&A format very well. One option could be to add this overarching documentation to tag info, although this might be hard to find (don't know the current SE stance on this). Another would be to make the question much broader (something that typically would be closed, but in this case it would have merit, since we can close all similar questions as a duplicate of this), and include all the questions you link to in the answer, possibly categorizing them.
Steven Jeuris
CC BY-SA 4.0
2018-06-18T13:40:06.937Z
I like how you summarized this. More selective and stable than a tag would be and yet organized in one place.  Here is a way chemistry SE did a summary of resources for learning chemistry: https://chemistry.stackexchange.com/q/37303/64817
Frank Hubeny
CC BY-SA 4.0
2018-06-18T16:36:42.740Z
@StevenJeuris let's discuss further in meta https://psychology.meta.stackexchange.com/q/2369/4397
Seanny123
<neuroscience><computational-modeling><neural-network><brain><artificial-intelligence>
The computer model of the brain
Arnon Weinberg
# The computer model of the brain

(Asked by: username_0 on 2018-06-17)

**I am a computer programmer or computer engineer, and am interested in comparing the brain to a classical computer in some way. How well does this comparison hold up?**

This is a general introduction to the computer/brain metaphor and its practical limitations. Similar questions on this forum include:

* [Difference between parallel processing done by human brain and by computers](https://psychology.stackexchange.com/q/1946/7001)
* [How does the brain generate answers to questions?](https://psychology.stackexchange.com/q/19573/7001)
* [What is the data structure used by the brain to store data?](https://psychology.stackexchange.com/q/8646/7001)
* [How is information stored in the brain](https://psychology.stackexchange.com/q/12642/7001)
* [How the Brain Does Garbage Collection in Real Time](https://psychology.stackexchange.com/q/19933/7001)
* [How does a human classify or cluster data?](https://psychology.stackexchange.com/q/12252/7001)
* [Human brain v/s computer: speed comparison?](https://psychology.stackexchange.com/q/15478/7001)
* [Does the brain have a 'debugging mode'?](https://psychology.stackexchange.com/q/12886/7001)
* [Why can't we use 100% of the brain in a certain moment?](https://psychology.stackexchange.com/q/8021/7001)
* [Is it possible to increase the capacity of human brain like “Lucy”?](https://psychology.stackexchange.com/q/12923/7001)
* [In what ways do our brains use compression?](https://psychology.stackexchange.com/q/12813/7001)
* [How many possible states does a brain have?](https://psychology.stackexchange.com/q/12406/7001)
* [What is the capacity of the human brain?](https://psychology.stackexchange.com/q/17463/7001)

**Tags:** neuroscience, computational-modeling, neural-network, brain, artificial-intelligence

> **Comments:**
> **username_1**: I understand what you are trying to do here, Arnon, but as is it does not seem to fit the Q&A format very well. One option could be to add this overarching documentation to tag info, although this might be hard to find (don't know the current SE stance on this). Another would be to make the question much broader (something that typically would be closed, but in this case it would have merit, since we can close all similar questions as a duplicate of this), and include all the questions you link to in the answer, possibly categorizing them.  
> **username_2**: I like how you summarized this. More selective and stable than a tag would be and yet organized in one place.  Here is a way chemistry SE did a summary of resources for learning chemistry: https://chemistry.stackexchange.com/q/37303/64817  
> **username_3**: @StevenJeuris let's discuss further in meta https://psychology.meta.stackexchange.com/q/2369/4397  

---

## 2 answers

### Answer by username_0 on 2018-06-17. Score: 6 | Accepted answer

This question's reference to a classical computer refers to a "[Turing Machine](https://en.wikipedia.org/wiki/Turing_machine)" style of computation, also known as a [knowledge system](https://en.wikipedia.org/wiki/Knowledge-based_systems), in which decisions and possible results are pre-programmed using if-statements, loops, and other logical constructs.

However, most modern computer programmers and engineers are at least somewhat familiar with neural networks, used for [machine learning](https://en.wikipedia.org/wiki/Machine_learning). [Artificial neural networks (ANNs)](https://en.wikipedia.org/wiki/Artificial_neural_network) are loosely based on the way the brain (a [biological neural network](https://en.wikipedia.org/wiki/Biological_neural_network)) works. If you want to use your computing background to understand the brain, then I recommend learning about neural networks instead. If you already have some understanding of neural networks, then think about how Siri or Watson work.

While neural networks can certainly be thought of as storing data, executing algorithms, parallel processing, and having memory and speed measurements, they are conceptually very different from classical computers, and generally uninstructive to compare. In particular, the "[von Neumann](https://en.wikipedia.org/wiki/Von_Neumann_architecture)" architectural style of modern computers does not have an analog in the brain (there is no separation between areas that compute and areas that store memories, for example). If you are still interested in such a comparison, then many of the questions listed above already have great answers, so check them out.

If you are interested in modeling the brain using a classical computing methodology, then check out [ACT-R](https://en.wikipedia.org/wiki/ACT-R), a popular architecture for modeling the brain using a modular, knowledge-based system (also see this [forum question](https://psychology.stackexchange.com/q/14064/7001)).

Artificial neural networks are not a perfect model for biological neural networks either, and if you are interested in the difference, then see this [forum question](https://psychology.stackexchange.com/q/7880/7001).

> **Comments:**
> **username_5**: I doubt that the question refers to a 'Turing Machine' style of computation (i.e. infinite tape with DFA control structure). I suspect programmers are thinking in terms of the [von Neumann architecture](https://en.wikipedia.org/wiki/Von_Neumann_architecture) instead. Of course, the point of CS is that these models (including ANNs) are equivalent & many of the questions we might ask about one translate over (upto some simulation factors) to the others. I would also be skeptical of encouraging engineers to think they'll learn something about the brain (nevermind mind) from looking at CS ANNs.  
> **username_0**: Comments are not for extended discussion; this conversation has been [moved to chat](https://chat.stackexchange.com/rooms/117459/discussion-on-answer-by-arnon-weinberg-the-computer-model-of-the-brain).  

---

### Answer by username_4 on 2018-07-01. Score: 1

This line of thought actually represents the current state of the art in scientific approaches to the brain. Before we discovered electricity and that (peripheral) neurons conduct electricity, one analogy or explanation suggested that the mind worked through hydraulic action.

It is therefore possible that we have to abandon this analogy, if we find a better one. For now, we give it explanatory power. However, we do know that the brain circuitry is very different from a computer. At the same time, neurons have discrete outputs, which can be represented in binary ([all-or-none](http://www.scholarpedia.org/article/FitzHugh-Nagumo_model#Absence_of_all-or-none_spikes)). (But there is also non-spiking neurons with a more continuous "analog" response.)

Suggested readings:

[Cognitive Science](https://plato.stanford.edu/entries/cognitive-science/#RepCom)

[Philosohpy of Mind: Computationalism](https://plato.stanford.edu/entries/computational-mind/)

[A classical argument against computationalism: The Chinese Room Argument](http://www.scholarpedia.org/article/Chinese_room_argument)


---

