The [other answer][1] gives a good overview of the differences between MLPs and CNNs, and it includes 2 diagrams that attempt to illustrate the main differences between MLPs and CNNs, i.e. weight sharing. However, these diagrams do not clarify what a **neuron** in a CNN could be. A better diagram, which illustrates what a neuron is in a CNN, from a CNN _and_ MLP perspective, is the following (taken from [the famous article on CNNs][3]).

[![enter image description here][2]][2]

Here, there are 2 main blocks (aka volumes): the orange block on the left (the input) and the blue/cyan volume on the right (the **feature maps**, i.e. the outputs of the convolutional layer, i.e. after the application of the convolutions with different kernels).

The _circles_ in the cyan block represent the neurons (or, more precisely, their activations or outputs). There are $k=5$ neurons stacked: this corresponds to the application of $k=5$ different kernels (i.e. weights) to that specific subset of the input (aka **receptive field**). So, these neurons, in the same stack, are looking at the same small subset of the input, but with different weights (i.e. kernels). The neurons, which are not shown in this diagram, that are on the same 2d plane (or slice) of the same neuron (e.g. the first that we see from left to right) in the cyan volume are the neurons that share the same weights, i.e. we use the same kernel to produce their outputs.

Some authors prefer to use the term [**convolutional networks**][5], i.e. without the term **neural**, probably because of this issue, i.e. it's not clear, especially to newcomers, what a neuron would be in a CNN, so the neuroscientific/biological view of CNNs is not always clear, although it's important to emphasize that [CNNs were inspired by the visual cortext][4].