<http://i60.tinypic.com/no7tye.png>
Fig. 1 Convolutional Neural Network (LeNet5)

On Convolutional Neural Network (LeNet 5), Fig. 1 proceeding of Convolution (C1), Max Pooling(Subsampling) (S2,S4) layers are computed by iterative manneur. But I did not understood how correctly proceed C3 (Convolution) layer. 

<http://tinypic.com/r/fvzp86/8>
Fig. 2 Proceeding C1 layer

Firstly as an input we recieve a MNIST 32\*32 grayscale image of number, perceiving it as an Array of Bytes of size 32\*32. In C1 layer we have 6 distinct(various) kernels filled with random small values. Each kernel from 1 to 6 is used to build 6 various feature maps (one kernel per one feature map). Moving receptive field of size 5\*5 one 1 pixel stride (bias) from left to right, multiplying value in image Array on kernel value adding bias and passing through sigmoid function. The result is i,j of a current constructed feature map. Once we have reached the end of Image Array we finished building of current feature map. 

<http://i57.tinypic.com/rk0jk9.jpg>
Fig. 3 Proceeding S2 layer

Next we start to produce S2 layer, again there will be 6 feature maps, as we using 2\*2 receptive field individually for each of 6 feature maps of C1 layer (using max pooling operations, selecting maximal value in 2\*2 receptive field). Proceeding of C1,S2,S4 conducting on iterative manneur. 

<http://i58.tinypic.com/ifsidu.png>
Fig. 4 Connection list of C3 layer

But next we need to compute C3 layer. According to various papers there exist a connection map. Could you please say what is perceived under connection list? Does this mean that we will still use 5\*5 receptive field as in C1 layer. And for example we see that in first row there is a marked feature maps corresponding to columns (0,4,5,6,9,10,11,12,14,15). Does this means that to construct 0,4,5,6,9,10,11,12,14,15 feature maps of C3 layer we will proceed convolutional operation under the first feature map of S2 layer with 5\*5 receptive field. What concrete kernel will be used during convolutional operation, or again we need to randomly generate 16 kernels filled with small numbers as we did it in C1 layer. If yes we see that feature maps 0,4,5,6,9,10,11,12,14,15 of C3 colored in light grey, light grey, dark grey, light grey, dark grey, light grey, dark grey, light grey, light grey, dark grey. It can be clearly see that first feature map of S2 is light grey but only 0,4,6,10,12,14 are colored in light grey. So maybe the building of 16 feature maps in C3 proceeding by different way. Could you please say how also produce C5 layer, will it have some certain connection list?