Simple feedforward neural network does not behave as intended - algorithm

Question

I'm having a hard time figuring out why my feed forward artificial neural networks almost always fail to learn my simple "OR" perceptron:
0 or 0 : 0
1 or 0 : 1
0 or 1 : 1
1 or 1 : 1
I tried various network configurations : 1 or 2 hidden layers, 2 to 8 neurons on hidden layers, a bias (value = 1, connected to each hidden neuron and output neuron)
For this example, the network looks like:
[1] [3]
[2] [4] [6]
[5]
input layer : [1] and [2]
hidden layer : [3] and [4] and [6]
output layer : [6]
Notations:
Neuron i : [i]
Weight between [i] and [j] : w(i,j)
[i] value : v(i)
[i] error: e(i)
sig(t) : 1/(1 + e^-t)
Weights initialization
Each weight is set to a random value between -0.5 and 0.5
Input
v(1) and v(2) are set with random values : 0 or 1
Propagate value (from left to right) :
v(3) = sig(v(1) * w(1,3) + v(2) * w(2,3))
v(4) = sig(v(1) * w(1,4) + v(2) * w(2,4))
v(5) = sig(v(1) * w(1,5) + v(2) * w(2,5))
v(6) = sig(v(3) * w(3,6) + v(4) * w(4,6) + v(5) * w(5,6))
expected output : 1 if (v(1) or v(2)) is true, 0 otherwise
e(6) = (expected - v(6)) * v(6) * (1 - v(6))
Propagate error (from right to left)
e(3) = e(6) * w(3,6) * v(3) * (1 - v(3))
e(4) = e(6) * w(4,6) * v(4) * (1 - v(4))
e(5) = e(6) * w(5,6) * v(5) * (1 - v(5))
Update weights (learning rate = 1)
w(1,3) = w(1,3) + v(1) * e(3)
w(1,4) = w(1,4) + v(1) * e(4)
w(1,5) = w(1,5) + v(1) * e(5)
w(2,3) = w(2,3) + v(2) * e(3)
w(2,4) = w(2,4) + v(2) * e(4)
w(2,5) = w(2,5) + v(2) * e(5)
w(3,6) = w(3,6) + v(3) * e(6)
w(4,6) = w(4,6) + v(4) * e(6)
w(5,6) = w(5,6) + v(5) * e(6)
Do it 300 times and print each result:
if v(6) > 0.5, it’s a True
if v(6) < 0.5, it’s a False
compare with expected output
After a few epoch, the network almost always return v(6) > 0.9, even for v(1) = v(2) = 0
Sometimes (roughly 1 out of 20 times), it works, the network learned correctly
What am I doing wrong ?
EDIT:
I found it !
The culprits was :
e(3) = e(6) * w(3,6) * v(3) * (1 - v(3))
e(4) = e(6) * w(4,6) * v(4) * (1 - v(4))
e(5) = e(6) * w(5,6) * v(5) * (1 - v(5))
Which should have been :
e(3) = e(6) * w(3,6)
e(4) = e(6) * w(4,6)
e(5) = e(6) * w(5,6)