I've recently been reading up on CNNs and this part of the architecture is really confusing me. Assume, I have an input of size [32*32*3] and pass it to a convolution layer. Now, if my kernel size were to be [5*5*3] and the depth of my convolution layer were to be 1, only one feature map would be produced for the image. Here, each neuron would have a 75 weights (+1 bias). If I wanted to calculate multiple feature maps in this layer, say 3, is each local section (in this example [5*5*3]) of the image looked on by three different neurons and each of their weights trained individually? And what would be the output volume of this layer?