<p>Here's my list of math subjects that support the study of brain (from a computational neuroscientist's perspective):</p>

<ul>
<li><strong>Linear algebra</strong>
<ul>
<li>to understand high dimensions, to compute things quickly, foundation for other math</li>
</ul></li>
<li><strong>Calculus</strong>
<ul>
<li>basics for everything continuous valued</li>
</ul></li>
<li><strong>Statistics</strong>
<ul>
<li>to analyze any data, you need stats!</li>
<li>basis for modeling, regression, clustering, classification, and all</li>
</ul></li>
<li><strong>Differential equations</strong> (basis for dynamical system)</li>
<li><strong>Dynamical system</strong>
<ul>
<li>intuition for neural dynamics (deterministic approximation)</li>
<li>modeling single neuron, synapse, small network</li>
</ul></li>
<li><strong>Statistical physics</strong>
<ul>
<li>modeling large scale noisy neural dynamics</li>
</ul></li>
<li><strong>Information theory</strong>
<ul>
<li>quantify how much "information" is coded in neural signal</li>
</ul></li>
<li><strong>Numerical computation</strong>
<ul>
<li>your data and algorithm needs to be implemented in a computer</li>
</ul></li>
<li><strong>Convex optimization</strong>
<ul>
<li>your statistics/model requires optimization</li>
</ul></li>
<li><strong>Probability theory</strong> (basis for stochastic process and statistics)</li>
<li><strong>Stochastic process</strong>
<ul>
<li>model of neural signals, decision process (diffusion), basis for advanced statistics</li>
<li>point process theory is useful for dealing with neural spike trains</li>
</ul></li>
<li><strong>Time series analysis</strong> (your data is a time series!)</li>
<li><strong>Signal detection theory</strong>
<ul>
<li>psychophysics is often designed to be a detection task</li>
</ul></li>
</ul>

<p>Brain is quite noisy, you need tools to deal with noise. More applied math than pure math is needed.</p>

<p>I have only seen topology being used a handful of times, and they were not very useful nor impressive. I love set theory and mathematical logic, but sadly never used it nor seen it being used.</p>

<p>In addition, real/complex/functional analyses are also useful, just in general.</p>

CC BY-SA 3.0
2013-10-18T20:26:00.000Z
This answer would be even more useful if, for each of the subjects you listed, you could give even just very brief examples of areas in cognitive science that would draw on these mathematical topics to some extent.
Jake Westfall
CC BY-SA 3.0
2013-10-18T23:27:10.123Z
@JakeWestfall thanks for your suggestion. I added a bit of details and made it community wiki. Feel free to edit it.
Memming
CC BY-SA 3.0
2014-02-01T22:48:49.887Z
@caseyr547 thanks for the negative vote!
Memming
CC BY-SA 3.0
2013-10-12T22:51:25.990Z
2013-10-18T23:26:45.683Z
2013-10-18T23:26:45.683Z
Memming
<p>I will deviate from the other answers and give more pessimistic response based on my experience as a mathematician and theoretical computer scientist that spends some of his time in a psychology department. </p>

<ol>
<li>In cognitive science, neuroscience, and psychology (like in most sciences) you will never <em>do</em> mathematics in the definition, lemma, theorem, proof sense. </li>
<li>Instead, <strong>you will rely on using (or modifying) mathematical tools others built</strong>, and doing calculations with them. (for a list of tools, <a href="https://cogsci.stackexchange.com/a/4780/29">see Memming's answer</a> and <a href="https://cogsci.stackexchange.com/q/288/29">more detail here on dynamic systems</a>)</li>
<li>Most of your colleagues will not know what you are doing (since the usual extent of training is linear regression),</li>
<li>and even the ones that are trained in mathematical tools will usually use them in a rather black-box way without making deep connections to other parts of mathematics.</li>
</ol>

<p>If you want to <em>do</em> mathematics and prove theorems then your are better off in one of the following fields allied to cognitive science:</p>

<ol>
<li>Mathematical linguistics -- although most people here still focus on tool use, there are some that prove things using ideas from category theory, mathematical logic, and a bit of topology (through <a href="http://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspondence#Curry.E2.80.93Howard.E2.80.93Lambek_correspondence" rel="nofollow noreferrer">Curry-Howard-Lambek crrespondence</a> and <a href="http://en.wikipedia.org/wiki/Stone_duality" rel="nofollow noreferrer">Stone duality</a>).</li>
<li>Economics -- most of the proofs here seem to be performances, but sometimes deep results are proven, usually related to game theory and decision theory. A solid grasp of analysis will be useful and topology will be relevant since most results follow from fixed-point theorems.</li>
<li><a href="https://cogsci.stackexchange.com/q/985/29">Computational learning theory</a> -- this subfield of theoretical computer science often can use very deep mathematics (usually from graph theory, combinatorics, optimization, discrete Fourier analysis, probability, and information theory) in its proofs, and has a history of interaction with cognitive science which has unfortunately waned in recent years.</li>
</ol>

CC BY-SA 3.0
2013-12-06T11:03:05.783Z
I'm coming from a theoretical physics background and after my PhD in computational chemistry I'm principally interested in going more in the cognitive science direction. (Specifically, I'm interested in notation for formal systems with the aim of more efficient learning/understanding of the subject matte by humans.) I'm very afraid of what you mention in your point 3. though, so I don't know "switching" in this area might kill me - I feel you don't learn much new math if you can't talk with anyone. Could you maybe reflect a little more on what you do, what you experience and recommend? Regard.
Nikolaj-K
CC BY-SA 3.0
2013-10-18T22:09:23.013Z
2013-10-18T22:09:23.013Z
2017-04-13T12:56:16.417Z
Artem Kaznatcheev
<p>I would say that the maths that are most useful in cognitive science are the ones that have to do with decision theory. So I would include linear algebra (with its matrixes, and "transition" or changes of state analysis), as well as probability and statistics, with their "expected values" and resulting decision trees. Computational and information analysis, with their "data structures" might also be helpful. The last thing I would include is optimization.</p>

CC BY-SA 3.0
2013-10-13T00:01:58.527Z
2013-10-13T00:01:58.527Z
Tom Au
<p>Which areas of mathematics can be formally studied in a cognitive science major? Or: which areas of mathematics can support the study of brain. Some areas that seem relevant would be: mathematical logic, graph theory, linear algebra. Is any element of topology relevant? Please direct to links/sources if possible. Is network science being used in these fields? I am asking this question, as I want to study further in mathematics, and would love it if I could do so while pursuing cognitive science.</p>

CC BY-SA 3.0
2013-10-11T16:25:46.983Z
This is a great book that is right up your alley.  http://www.amazon.com/Networks-of-the-Brain-ebook/dp/B005AT9ZOI/ref=sr_1_1?ie=UTF8&qid=1381508643&sr=8-1&keywords=networks+and+the+brain  The caveat is that the graph theory might not be as rigorous as you are used to, but the field is wide open for those with a theoretical background.
Chuck Sherrington
CC BY-SA 3.0
2013-10-11T16:28:32.493Z
There are a couple of other questions kicking around about this very topic.  It's not directly in line with your interests, but there's some discussion in this question: http://cogsci.stackexchange.com/questions/4578/is-biophysics-of-computation-still-a-good-book
Chuck Sherrington
CC BY-SA 3.0
2013-10-23T14:40:16.187Z
@Artem (I cannot comment to your answer) Do you suggest that I should apply to a math or computer science grad program instead? I have a background in electrical, so it's not highly related. I came across these papers here - Category theory for AI : http://www.j-paine.org/dobbs/why_be_interested_in_categories.html
Topological foundations of Cognitive Science : http://ontology.buffalo.edu/smith/articles/topo.html Though I understand that mathematical maturity cannot be achieved merely by using it as a tool, which was something that left me dissatisfied in my electrical engineering days. What is
Zeph
CC BY-SA 3.0
2013-10-23T17:47:29.350Z
If you are in a place that has research based masters programs (like Canada or the UK) then I would recommend getting a masters in math or computer science, and then doing your PhD in theoretical cognitive science after. It will let you experience math, get some good skills, and then decide if you still like it. Feel free to email me (my address should be easy to find with a few clicks), and we can chat about it.
Artem Kaznatcheev
<mathematical-psychology>
What areas of mathematics support the study of cognitive science?
Zeph
# What areas of mathematics support the study of cognitive science?

(Asked by: username_0 on 2013-10-11)

Which areas of mathematics can be formally studied in a cognitive science major? Or: which areas of mathematics can support the study of brain. Some areas that seem relevant would be: mathematical logic, graph theory, linear algebra. Is any element of topology relevant? Please direct to links/sources if possible. Is network science being used in these fields? I am asking this question, as I want to study further in mathematics, and would love it if I could do so while pursuing cognitive science.

**Tags:** mathematical-psychology

> **Comments:**
> **username_1**: This is a great book that is right up your alley.  http://www.amazon.com/Networks-of-the-Brain-ebook/dp/B005AT9ZOI/ref=sr_1_1?ie=UTF8&qid=1381508643&sr=8-1&keywords=networks+and+the+brain  The caveat is that the graph theory might not be as rigorous as you are used to, but the field is wide open for those with a theoretical background.  
> **username_1**: There are a couple of other questions kicking around about this very topic.  It's not directly in line with your interests, but there's some discussion in this question: http://cogsci.stackexchange.com/questions/4578/is-biophysics-of-computation-still-a-good-book  
> **username_0**: @Artem (I cannot comment to your answer) Do you suggest that I should apply to a math or computer science grad program instead? I have a background in electrical, so it's not highly related. I came across these papers here - Category theory for AI : http://www.j-paine.org/dobbs/why_be_interested_in_categories.html Topological foundations of Cognitive Science : http://ontology.buffalo.edu/smith/articles/topo.html Though I understand that mathematical maturity cannot be achieved merely by using it as a tool, which was something that left me dissatisfied in my electrical engineering days. What is  
> **username_2**: If you are in a place that has research based masters programs (like Canada or the UK) then I would recommend getting a masters in math or computer science, and then doing your PhD in theoretical cognitive science after. It will let you experience math, get some good skills, and then decide if you still like it. Feel free to email me (my address should be easy to find with a few clicks), and we can chat about it.  

---

## 3 answers

### Answer by username_3 on 2013-10-12. Score: 9

Here's my list of math subjects that support the study of brain (from a computational neuroscientist's perspective):

* **Linear algebra**
  + to understand high dimensions, to compute things quickly, foundation for other math
* **Calculus**
  + basics for everything continuous valued
* **Statistics**
  + to analyze any data, you need stats!
  + basis for modeling, regression, clustering, classification, and all
* **Differential equations** (basis for dynamical system)
* **Dynamical system**
  + intuition for neural dynamics (deterministic approximation)
  + modeling single neuron, synapse, small network
* **Statistical physics**
  + modeling large scale noisy neural dynamics
* **Information theory**
  + quantify how much "information" is coded in neural signal
* **Numerical computation**
  + your data and algorithm needs to be implemented in a computer
* **Convex optimization**
  + your statistics/model requires optimization
* **Probability theory** (basis for stochastic process and statistics)
* **Stochastic process**
  + model of neural signals, decision process (diffusion), basis for advanced statistics
  + point process theory is useful for dealing with neural spike trains
* **Time series analysis** (your data is a time series!)
* **Signal detection theory**
  + psychophysics is often designed to be a detection task

Brain is quite noisy, you need tools to deal with noise. More applied math than pure math is needed.

I have only seen topology being used a handful of times, and they were not very useful nor impressive. I love set theory and mathematical logic, but sadly never used it nor seen it being used.

In addition, real/complex/functional analyses are also useful, just in general.

> **Comments:**
> **username_4**: This answer would be even more useful if, for each of the subjects you listed, you could give even just very brief examples of areas in cognitive science that would draw on these mathematical topics to some extent.  
> **username_3**: @JakeWestfall thanks for your suggestion. I added a bit of details and made it community wiki. Feel free to edit it.  
> **username_3**: @caseyr547 thanks for the negative vote!  

---

### Answer by username_2 on 2013-10-18. Score: 9

I will deviate from the other answers and give more pessimistic response based on my experience as a mathematician and theoretical computer scientist that spends some of his time in a psychology department.

1. In cognitive science, neuroscience, and psychology (like in most sciences) you will never *do* mathematics in the definition, lemma, theorem, proof sense.
2. Instead, **you will rely on using (or modifying) mathematical tools others built**, and doing calculations with them. (for a list of tools, [see username_3's answer](https://cogsci.stackexchange.com/a/4780/29) and [more detail here on dynamic systems](https://cogsci.stackexchange.com/q/288/29))
3. Most of your colleagues will not know what you are doing (since the usual extent of training is linear regression),
4. and even the ones that are trained in mathematical tools will usually use them in a rather black-box way without making deep connections to other parts of mathematics.

If you want to *do* mathematics and prove theorems then your are better off in one of the following fields allied to cognitive science:

1. Mathematical linguistics -- although most people here still focus on tool use, there are some that prove things using ideas from category theory, mathematical logic, and a bit of topology (through [Curry-Howard-Lambek crrespondence](http://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspondence#Curry.E2.80.93Howard.E2.80.93Lambek_correspondence) and [Stone duality](http://en.wikipedia.org/wiki/Stone_duality)).
2. Economics -- most of the proofs here seem to be performances, but sometimes deep results are proven, usually related to game theory and decision theory. A solid grasp of analysis will be useful and topology will be relevant since most results follow from fixed-point theorems.
3. [Computational learning theory](https://cogsci.stackexchange.com/q/985/29) -- this subfield of theoretical computer science often can use very deep mathematics (usually from graph theory, combinatorics, optimization, discrete Fourier analysis, probability, and information theory) in its proofs, and has a history of interaction with cognitive science which has unfortunately waned in recent years.

> **Comments:**
> **username_5**: I'm coming from a theoretical physics background and after my PhD in computational chemistry I'm principally interested in going more in the cognitive science direction. (Specifically, I'm interested in notation for formal systems with the aim of more efficient learning/understanding of the subject matte by humans.) I'm very afraid of what you mention in your point 3. though, so I don't know "switching" in this area might kill me - I feel you don't learn much new math if you can't talk with anyone. Could you maybe reflect a little more on what you do, what you experience and recommend? Regard.  

---

### Answer by username_6 on 2013-10-13. Score: 3

I would say that the maths that are most useful in cognitive science are the ones that have to do with decision theory. So I would include linear algebra (with its matrixes, and "transition" or changes of state analysis), as well as probability and statistics, with their "expected values" and resulting decision trees. Computational and information analysis, with their "data structures" might also be helpful. The last thing I would include is optimization.


---

