  This paper describes a new model for an artificial neural network processing
unit or neuron. It is slightly different to a traditional feedforward network
by the fact that it favours a mechanism of trying to match the wave-like
'shape' of the input with the shape of the output against specific value error
corrections. The expectation is then that a best fit shape can be transposed
into the desired output values more easily. This allows for notions of
reinforcement through resonance and also the construction of synapses.
