Channels and neurons in a CNN
out_channels can be thought of as the number of features/filters the layer can learn. So, for eg., if a layer of a CNN has out_channels set to 64, it can &quot;learn&quot; 64 features. These features could be edges/patterns. This is a hyper-parameters and can set to an arbitrary number.
The activation of a neuron in a layer will be high if the input to that layer contains the feature learned by the layer, that's how a convolution (or correlation) function works.
In a CNN, a neuron is the result of computing the dot product of the input and the filter/kernel at every window of the convolution.