What makes different neurons calculate different parameters within a layer?

[Christian_Simonis]

Hi @Rohit_Pandey1

welcome to the community and thanks for your question!

Here you can find an illustration that might be relevant for you:

Choice of activation function

In addition to the very good answers for illustrative purposes (although it may be obvious to many - but it helped several students when dealing with ReLU for the first time): [image] Since many neurons are assigned with bias and weights, linked with an activation function, by combination of multiple neurons (as the neural net in total does) this also allows to learn highly nonlinear behavior, although the activation function of one neuron itself (as you correctly pointed out) possesses only…

You can see that each neuron has individual weights and bias that are learned by the neuron within the training process. These parameters are different for each neuron and they lead (via activation function) also to a different output of each neuron.

Best regards
Christian