We see that there are 5 neurons connected to the same region in, what you call, "space" (but that I would have called "2D space"). This picture suggests that all the 5 neurons share the same weights, because they are grouped in a parallelepiped which is connected (through lines) to the same 2D space (or receptive field). However, this is not consistent with the following explanations:
every entry in the 3D output volume can also be interpreted as an output of a neuron that looks at only a small region in the input and shares parameters with all neurons to the left and right spatially (since these numbers all result from applying the same filter)
According to the explanations, neurons that share the same weights are the ones that share the same kernel (the words "weights" and "kernel" are, in this context, used interchangeably, in fact). However, these neurons that share the same kernel are not looking at the same receptive field: even though those receptive fields may overlap, they are not exactly the same. The neurons that look at the exact same receptive field are associated with different kernels (or weights).
So, either the explanation above is wrong or the picture is at least very misleading (and I would say wrong). So, I would suggest that you change that picture or you change the explanations to make them consistent.
In the following figure (from the chapter Convolutional Neural Networks)
We see that there are 5 neurons connected to the same region in, what you call, "space" (but that I would have called "2D space"). This picture suggests that all the 5 neurons share the same weights, because they are grouped in a parallelepiped which is connected (through lines) to the same 2D space (or receptive field). However, this is not consistent with the following explanations:
According to the explanations, neurons that share the same weights are the ones that share the same kernel (the words "weights" and "kernel" are, in this context, used interchangeably, in fact). However, these neurons that share the same kernel are not looking at the same receptive field: even though those receptive fields may overlap, they are not exactly the same. The neurons that look at the exact same receptive field are associated with different kernels (or weights).
So, either the explanation above is wrong or the picture is at least very misleading (and I would say wrong). So, I would suggest that you change that picture or you change the explanations to make them consistent.