From this site,
The output node has a "threshold" t.
Rule:
If summed input ≥ t, then it "fires" (output y = 1).
Else (summed input < t) it doesn't fire (output y = 0).
How y equals to zero. Any Ideas appreciated.
Neural networks have a so called "activation function", it's usually some form of a sigmoid-like function to map the inputs into separate outputs.
http://zephyr.ucd.ie/mediawiki/images/b/b6/Sigmoid.png
For you it happens to be either 0 or 1 and using a comparison instead of a sigmoid function,
so your activation curve will be even sharper than the graph above. In the said graph, your t, the threshold, is 0 on the X axis.
So as pseudo code :
sum = w1 * I1 + w2 + I2 + ... + wn * In
sum is the weighted sum of all in the inputs the neuron, now all you have to do is compare that sum to t, the threshold :
if sum >= t then y = 1 // Your neuron is activated
else y = 0
You can use the last neuron's output as the networks output to predict something into 1/0, true/false etc.
If you're studying NNs, I'd suggest you start with the XOR problem, then it will all make sense.