How neural networks handle large variations in the input? I am reading the paper with the title "Classification of indoor actions through deep neural networks". And I came across this statement:


  With this mechanism very complex functions can be learned combining
  these modules: the resulting networks are often very sensitive to
  minute details and insensitive to large irrelevant variations.


I have read a lot about deep neural network. However, I might have missed the logic behind why deep neural networks are insensitive to large irrelevant features.
They can handle large variations of inputs because the neurons have weights and those weights get optimized as part of learning a good model. So even though the model might take in a value that ranges from 0 to 100,000, if it isn't relevant to predicting the outcome, it will have a very small effect.