Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-15T17:27:07.237Z
Answers.LastActivityDate: 2018-08-15T17:27:07.237Z
Answers.UserDisplayName: John Doucette
Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-08-15T19:28:46.793Z
Answers.LastActivityDate: 2018-08-15T19:28:46.793Z
Answers.UserDisplayName: Daniel
Body: <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>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-01-21T00:15:34.833Z
Comments.Text: 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.
Comments.UserDisplayName: nbro
Tags: <neural-networks><terminology><multilayer-perceptrons><function-approximation>
Title: Is a multilayer perceptron a recursive function?
UserDisplayName: user3352632
ThreadText: # 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.  
  
  ---
  
  ## 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.
  
  
  ---
  
  ### 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?
  
  
  ---
  
  