<p>Inherently, no. The MLP is just a data structure. It represents a function, but a standard MLP is just representing an input-output mapping, and there's no recursive structure to it.</p>

<p>On the other hand, possibly your source is referring to the common <em>algorithms</em> that operate over MLPs, specifically forward propagation for prediction and back propagation for training. Both of these algorithms are easy to think about recursively, with each node performing a sort of recursive call with its children or parents as the target, and some useful information about activations or errors attached. I actually encourage my students to implement it recursively for this reason, even though it's probably not the most efficient solution.</p>

CC BY-SA 4.0
2018-08-15T17:27:07.237Z
2018-08-15T17:27:07.237Z
John Doucette
<p>Sure, you can define plenty of things we don't generally need to regard as recursive as so. An MLP is just a series of functions applied to its input. This can be loosely formulated as </p>

<p>$$ o_n = f(o_{n-1})$$</p>

<p>Where $o_n$ is the output of layer $n$. </p>

<p>But this clearly doesn't reveal, much does it?</p>

CC BY-SA 4.0
2018-08-15T19:28:46.793Z
2018-08-15T19:28:46.793Z
Daniel
<p>I read somewhere that a <em>multilayer perceptron</em> is a recursive function in its forward propagation phase. I am not sure, what is the recursive part? For me, I would see an MLP as a chained function. So, it would nice anyone could relate an MLP to a recursive function.</p>

CC BY-SA 4.0
2021-01-21T00:15:34.833Z
Are you sure the source was not referring to "recurrent neural networks"? Without having a link to the specific resource, it's difficult to answer this question, although both of the interpretations below look plausible to me.
nbro
<neural-networks><terminology><multilayer-perceptrons><function-approximation>
Is a multilayer perceptron a recursive function?
user3352632
# Is a multilayer perceptron a recursive function?

(Asked by: username_0 on 2018-08-15)

I read somewhere that a *multilayer perceptron* is a recursive function in its forward propagation phase. I am not sure, what is the recursive part? For me, I would see an MLP as a chained function. So, it would nice anyone could relate an MLP to a recursive function.

**Tags:** neural-networks, terminology, multilayer-perceptrons, function-approximation

> **Comments:**
> **username_1**: Are you sure the source was not referring to "recurrent neural networks"? Without having a link to the specific resource, it's difficult to answer this question, although both of the interpretations below look plausible to me.  

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## 2 answers

### Answer by username_2 on 2018-08-15. Score: 5

Inherently, no. The MLP is just a data structure. It represents a function, but a standard MLP is just representing an input-output mapping, and there's no recursive structure to it.

On the other hand, possibly your source is referring to the common *algorithms* that operate over MLPs, specifically forward propagation for prediction and back propagation for training. Both of these algorithms are easy to think about recursively, with each node performing a sort of recursive call with its children or parents as the target, and some useful information about activations or errors attached. I actually encourage my students to implement it recursively for this reason, even though it's probably not the most efficient solution.


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### Answer by username_3 on 2018-08-15. Score: 3

Sure, you can define plenty of things we don't generally need to regard as recursive as so. An MLP is just a series of functions applied to its input. This can be loosely formulated as

$$ o\_n = f(o\_{n-1})$$

Where $o\_n$ is the output of layer $n$.

But this clearly doesn't reveal, much does it?


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