Why does every graph of neural networks always illustrate a layer of neurons only communicating forward by *one* layer of neurons instead of multiple layers forward simultaneously?

Question

Why does every graph of neural networks always illustrate a layer of neurons only communicating forward by *one* layer of neurons instead of multiple layers forward simultaneously?

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Answer 1

It’s because the neural networks you are seeing are ones that we’ve found to be useful. There are plenty of different types but to make them practical to compute and train optimization gets done that doesn’t tie in well with more interesting architectures.

I mean for a long time the single hidden layer fully connected network was the only real practical path.

It’s not like they don’t exist through. If you want to see more interesting stuff you need to look into some of the deep neural network architectures.

Here’s a description of YOLOv3 one of the really popular object detectors.

https://bestinau.com.au/yolov3-architecture-best-model-in-object-detection/amp/

And some variant of YOLOv3 specializing in pedestrians I think.

https://www.spiedigitallibrary.org/ContentImages/Journals/JEIME5/29/5/053002/FigureImages/JEI_29_5_053002_f004.png