This is the best I can come up with for now. My scattered brain and I can't seem to get it together to come up with something better. Oh well, this is only the beginning. I will hope to have a method to determine the highest and lowest interval that I can sneak in there. This is just the main bulk of how the neurons will be activated by the weights of their connections. How this will come together completely is another question but I'm excited that I've made it this far.

A simplified pseudo-code for averaging and activation function will be something close to the following:

i = neurons being read; j = neurons reading; x = first neuron in a column; y = last neuron in a column;
for j = x to y ++1;
    A_j = 0; h1 = 0; h2 = 0; ht = 0; F_t = 0;
    for t = 1 to 8 ++1;
        for i = x to y ++1;
            h1 = h1 + A_i*W_(i,t);
            h2 = h2 + A_i;
        next i;
    if h2 == 0 then ht = 0 else ht = h1/h2
    if a_(i,t) ≤ ht and ht ≤ b_(i,t) then F_t = 1 and A_j = 1;
    next t;
    for t = 1 to 8 ++1;
        if F_t = 1 then
            for p = 1 to 8 ++1;
                if F_p = 1 then change(t, p, j);
            next p
        next t;
next j

change(t, p, j)
    switch (t);
    case
        (t = 1) : b_(j,p) = b_(j,p) + 1;
        (t = 2) : b_(j,p) = b_(j,p) - 1;
        (t = 3) : a_(j,p) = a_(j,p) + 1;
        (t = 4) : a_(j,p) = a_(j,p) + 1;
        (t = 5) : W_(intervalH(j),p) = W_(intervalH(j),p) + 1 of highest interval;
        (t = 6) : W_(intervalH(j),p) = W_(intervalH(j),p) - 1 of highest interval;
        (t = 7) : W_(intervalL(j),p) = W_(intervalL(j),p) + 1 of lowest interval;
        (t = 8) : W_(intervalL(j),p) = W_(intervalL(j),p) - 1 of lowest interval;
    break
return

Note: I was pleasantly surprised that the code snippet function recognize my pseudo code as VBscript. I was just throwing things together, who knew?

Edit: Updated the code to reflect the use of functions that can determine the higher and lower intervals.

Edit: Updated the code to temporarily deactivate the neurons while they read their input of weights. This will make sure they they will only active when their intervals are triggered and not have to go through the process of flipping their status for each individual weight check. Even if the neuron was active before and is later switched off, the interval will still trigger it back to its active state because it is checking the active status of neurons in the preceding layer. So, as long as it was active before, it will still go back to its active state so long as nothing has changed that would cause it to deactivate (all of the intervals fail to trigger).