Lets say I have a Neural Network with 5 layers, including input and output layer. Each Layer has 5 nodes. Assume the Layers are fully connected, but the 3rd Node in the 2nd Layer is connected to the 5th node in the 4th Layer. All these numbers are chosen at random for the example.

My question is when is the 5th node in the 4th layer fed forward? Lets go through it step by step: the first layer is normally fed forward to the second. the second layer is normally fed forward to the third, but the 3rd node is also fed forward to the 5th node of the 4th layer. So the problem here is, is the 5th node in the 4th layer now fed forward or is it fed forward when the 3rd layer is done being fed forward? The 1st method would mean that the node would get fed forward 2 times and my concern is, if the output is still valid. Further more it would also come to 2 asynchronous outputs and how would these be interpreted?

Because in the Brain, I heard, the neurons are fired when an impulse arrives so this would equal the 1st method.

Let's say I have a neural network with 5 layers, including the input and output layer. Each layer has 5 nodes. Assume the layers are fully connected, but the 3rd node in the 2nd layer is connected to the 5th node in the 4th layer. All these numbers are chosen at random for the example.

When is the 5th node in the 4th layer fed forward?

Let's go through it step by step. The first layer is normally fed forward to the second. The second layer is normally fed forward to the third, but the 3rd node is also fed forward to the 5th node of the 4th layer.

At this point, is the 5th node in the 4th layer now fed forward, or is it fed forward when the 3rd layer is done being fed forward? The 1st method would mean that the node would get fed forward 2 times and my concern is, if the output is still valid.

Furthermore, it would also come to 2 asynchronous outputs and how would these be interpreted?

Because in the brain, I heard, the neurons are fired when an impulse arrives so this would equal the 1st method.

If I have a Network with not fully connected layers. Futher more there are connections spanning over multiple layers e.g from a Node in layer 1 to a Node on layer 4.

My question is if I have a Node with inputs from diffrent Layers, does the Node wait before feeding forward until all inputs have arrived, or does the Node feed Forward after the first inputs arrive?

In this case of course I would treat inputs from the same Layer the same in respect to the arrival time. So they would be added together.

If the second is true then the node would feed forward multiple times in the same iteration. Would this still be a valid result of the Network?

Lets say I have a Neural Network with 5 layers, including input and output layer. Each Layer has 5 nodes. Assume the Layers are fully connected, but the 3rd Node in the 2nd Layer is connected to the 5th node in the 4th Layer. All these numbers are chosen at random for the example.

My question is when is the 5th node in the 4th layer fed forward? Lets go through it step by step: the first layer is normally fed forward to the second. the second layer is normally fed forward to the third, but the 3rd node is also fed forward to the 5th node of the 4th layer. So the problem here is, is the 5th node in the 4th layer now fed forward or is it fed forward when the 3rd layer is done being fed forward? The 1st method would mean that the node would get fed forward 2 times and my concern is, if the output is still valid. Further more it would also come to 2 asynchronous outputs and how would these be interpreted?

Because in the Brain, I heard, the neurons are fired when an impulse arrives so this would equal the 1st method.