* Artificial neural networks are modelled from biological neurons.
* The connections of the biological neuron are modeled as weights.
* A positive weight reflects an excitatory connection, while negative values mean inhibitory connections.
* All inputs are modified by a weight and summed. This activity is referred to as a linear combination.
* Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
