How to interpret NEAT-Python's output

I'm learning about Neural Networks using the NEAT algorithm. To understand the results I'm tryling to reproduce the calculations for a few values by hand using Excel.

Running the XOR demo of NEAT-Python I get the following results and the corresponding graph:

Fitness: 3.93262784315
Nodes:
        0 DefaultNodeGene(key=0, bias=-4.28711170619, response=1.0, activation=sigmoid, aggregation=sum)
        1197 DefaultNodeGene(key=1197, bias=-1.20497802851, response=1.0, activation=sigmoid, aggregation=sum)
Connections:
        DefaultConnectionGene(key=(-2, 0), weight=-1.83815166978, enabled=True)
        DefaultConnectionGene(key=(-2, 1197), weight=1.7368731859, enabled=True)
        DefaultConnectionGene(key=(-1, 0), weight=5.72212927441, enabled=True)
        DefaultConnectionGene(key=(-1, 1197), weight=-1.30171417401, enabled=True)
        DefaultConnectionGene(key=(1197, 0), weight=9.27762332932, enabled=True)

Output:
input (0.0, 0.0), expected output (0.0,), got [5.485892844789737e-10]
input (0.0, 1.0), expected output (1.0,), got [0.9999970321654681]
input (1.0, 0.0), expected output (1.0,), got [0.9992352946960754]
input (1.0, 1.0), expected output (0.0,), got [0.2595603437828634]

Graph of Neural Network

Although I understand the basics of NN, I don't get the results of the command line: At first I calculate the value of node 1197 with the example of (0, 0):

-1.3 * 0 +1.7 * 0 -1.2 (bias)
= -1.2
sigmoid(-1.2) = 0.231475... (result of node 1197)

And then the final result:

5.7 * 0 +9.3 * 0.231475 (result node 1197) -1.8 * 0 -4.3 (bias)
=-2.147282...
sigmoid(-2.147282) = 0.104585...

Which is definitely not equal to the expected 5.485892844789737e-10. Can anyone find an error in my calculations?

Solution

For everyone who encounters the same: I debugged the code line by line and found out that the sigmoid activation function is defined as follows:

def sigmoid_activation(z):
    z = max(-60.0, min(60.0, 5.0 * z))
    return 1.0 / (1.0 + math.exp(-z))

I'm not entirely sure about the purpose of this "normalization", but this factor of 5 caused my difficulties. With the first line commented out everything works as expected.