With many of the ancillary, but important, functions coded, we now turn our attention to the meat of the neural network, the network itself. Within a neural network, the network part is an all-encompassing universe. Everything resides within it. Within this structure we will need to store the input, output, and Hidden Layers of neurons, as well as the learning rate and Momentum, as follows:

public class Network
{
      public double LearningRate{ get; set; }
      public double Momentum{ get; set; }
      public List<Neuron>InputLayer{ get; set; }
      public List<List<Neuron>>HiddenLayers{ get; set; }
      public List<Neuron>OutputLayer{ get; set; }
      public List<Neuron>MirrorLayer {get; set; }
      public List<Neuron>CanonicalLayer{ get; set; }