Last call to make your voice heard! Our 2022 Developer Survey closes in less than a week. Take survey.

3 of 3
nbro
• 33.2k
• 8
• 72
• 137

tl;dr The equivalent to a neuron in a Fully-Connected (FC) layer is the kernel (or filter) of a Convolution layer

### Differences

The neurons of these two types of layers have two key differences. These are that the convolution layers implement:

• Sparse connectivity, i.e. each neuron is connected only to an area of the input, not the whole.
• Weight sharing, i.e. similar connections end up having the same weights. This is usually visualized as the same filter traversing the image.

Besides these two key differences, there are some other technical details, e.g. how the biases are implemented. Other than that they perform the same operation.

What causes some confusion is that the input of a CNN is usually 2 or 3-dimensional, while a FC is usually 1-dimensional. These aren't mandatory however. To better help visualize the differences between the two I made a couple of figures illustrating the differences between a conv-layer and a FC one, both in 1D.

### Sparse connectivity

On the left are two FC neural networks. On the right, are layers with sparse connections.

### Weight sharing

On the left is a sparsely connected network. The colors represent the different values of the weights. On the right is the same network with weight-sharing. Note that similar weights (i.e. arrows with the same direction in each layer) have the same value.