Answers.Body: <p>It depends on the architecture of the neural network. However, in general, no, neurons at layer <span class="math-container">$l$</span> are not only affected by neurons at layer <span class="math-container">$l-1$</span>.</p>
  
  <p>In the case of a multi-layer perceptron (or feed-forward neural network), only neurons at layer <span class="math-container">$l-1$</span> <strong>directly</strong> affect the neurons at layer <span class="math-container">$l$</span>. However, neurons at layers <span class="math-container">$l-i$</span>, for <span class="math-container">$i=2, \dots, l$</span>, also <em>indirectly</em> affect the neurons at layer <span class="math-container">$l$</span>.</p>
  
  <p>In the case of recurrent neural networks, the output of neuron <span class="math-container">$j$</span> at level <span class="math-container">$l$</span> can also affect the same neuron but at a different time step.</p>
  
  <p>In the case of <a href="https://arxiv.org/abs/1512.03385" rel="noreferrer">residual networks</a>, the output of a neuron at a layer <span class="math-container">$l-i$</span>, for <span class="math-container">$i=2, \dots, l$</span>, can <strong>directly</strong> affect the neurons at layer <span class="math-container">$l$</span>. These non-neighboring connections are called <strong>skip connections</strong> because they skip layers.</p>
  
  <p>There are probably other combinations of connections between neurons at different layers or the same layer. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-12-20T20:54:25.573Z
Answers.LastActivityDate: 2019-12-20T21:04:34.073Z
Answers.LastEditDate: 2019-12-20T21:04:34.073Z
Answers.UserDisplayName: nbro
Body: <p>Are artificial neurons in layer <span class="math-container">$l$</span> only affected by those in layer <span class="math-container">$l-1$</span> (providing inputs) or are they also affected by neurons in layer <span class="math-container">$l$</span> (and maybe by neurons in other layers)?</p>
  
Tags: <neural-networks><machine-learning><artificial-neuron>
Title: Are neurons in layer $l$ only affected by neurons in the previous layer?
UserDisplayName: George White
ThreadText: # Are neurons in layer $l$ only affected by neurons in the previous layer?
  
  (Asked by: username_0 on 2019-12-20)
  
  Are artificial neurons in layer $l$ only affected by those in layer $l-1$ (providing inputs) or are they also affected by neurons in layer $l$ (and maybe by neurons in other layers)?
  
  **Tags:** neural-networks, machine-learning, artificial-neuron
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-12-20. Score: 5 | Accepted answer
  
  It depends on the architecture of the neural network. However, in general, no, neurons at layer $l$ are not only affected by neurons at layer $l-1$.
  
  In the case of a multi-layer perceptron (or feed-forward neural network), only neurons at layer $l-1$ **directly** affect the neurons at layer $l$. However, neurons at layers $l-i$, for $i=2, \dots, l$, also *indirectly* affect the neurons at layer $l$.
  
  In the case of recurrent neural networks, the output of neuron $j$ at level $l$ can also affect the same neuron but at a different time step.
  
  In the case of [residual networks](https://arxiv.org/abs/1512.03385), the output of a neuron at a layer $l-i$, for $i=2, \dots, l$, can **directly** affect the neurons at layer $l$. These non-neighboring connections are called **skip connections** because they skip layers.
  
  There are probably other combinations of connections between neurons at different layers or the same layer.
  
  
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