Sorry if that is a dumb question. I just started to learn about machine learning.
I'm reading this book about neural networks: http://neuralnetworksanddeeplearning.com/chap1.html#a_simple_network_to_classify_handwritten_digits
It explains how an artificial neural network classifies handwritten digits applying weights and biases to inputs and each input is assigned a value between 0 and 1, with 0 being white pixels and 1 black pixels.
Now let's say an image containing only black pixels is input. If I understand it well this would cause all neurons in the hidden layer to output 1, which means that all neurons in output layer will output 1. This is not the intended behavior.
What is the correct way to model an artificial neuron that outputs 0 when 1 is input and outputs 1 when 0 is input? Is this done by assigning a negative weight or using a different activation function?