As stated previously, a biological neuron provides the outputs to multiple cells. If we continue to use the example in the previous section, our neuron should forward the attained value of 0.1 to multiple cells. For this sake of this situation, let's assume that there are three neurons.

If we provide the same output of 0.1 to all the neurons, they will all give us the same output, which isn't really useful. The question that now begs an answer is why we need to provide this to three or multiple neurons, when we could do it with only one? 

To make this computationally useful, we apply some weights, where each weight will have a different value. We multiply the activation function with these weights to gain different values for each neuron. Look at the example depicted in the following diagram:

Here, we can clearly see that we assign the values =2, =-1, and =3 to the three weights and obtain the outputs =0.2, =-0.1, and =0.3. We can actually connect these different values to three neurons and the output achieved will be different.